Utilizing a lesson package in a virtual world

ABSTRACT

A method for execution by a computing entity to utilize a lesson package in a virtual world environment includes selecting the lesson package based on a learner requirement for a learner to produce a selected lesson package. The method further includes identifying a set of active virtual world environments that each include a different instance of execution of the selected lesson package. The method further includes selecting one active virtual world environment of the set of active virtual world environments based on at least one of the learner requirement and learning assessment results for the selected lesson package to produce a selected virtual world environment. The method further includes rendering updated first descriptive asset video frames of a first descriptive asset and updated second descriptive asset video frames of a second descriptive asset within the selected virtual world environment to produce a new video stream for the learner.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation in part of U.S. Utility applicationSer. No. 17/395,610, entitled “UPDATING A LESSON PACKAGE,” filed Aug. 6,2021, pending, which claims priority to U.S. Provisional Application No.63/064,742, entitled “UPDATING A LESSON PACKAGE,” filed Aug. 12, 2020,expired, all of which are hereby incorporated herein by reference intheir entirety and made part of the present U.S. Utility PatentApplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer systems and moreparticularly to computer systems providing educational, training, andentertainment content.

Description of Related Art

Computer systems communicate data, process data, and/or store data. Suchcomputer systems include computing devices that range from wirelesssmart phones, laptops, tablets, personal computers (PC), work stations,personal three-dimensional (3-D) content viewers, and video gamedevices, to data centers where data servers store and provide access todigital content. Some digital content is utilized to facilitateeducation, training, and entertainment. Examples of visual contentincludes electronic books, reference materials, training manuals,classroom coursework, lecture notes, research papers, images, videoclips, sensor data, reports, etc.

A variety of educational systems utilize educational tools andtechniques. For example, an educator delivers educational content tostudents via an education tool of a recorded lecture that has built-infeedback prompts (e.g., questions, verification of viewing, etc.). Theeducator assess a degree of understanding of the educational contentand/or overall competence level of a student from responses to thefeedback prompts.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2A is a schematic block diagram of an embodiment of a computingentity of a computing system in accordance with the present invention;

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of another embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an environmentsensor module of a computing system in accordance with the presentinvention;

FIG. 5A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 7A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience in accordance with the present invention;

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system in accordance with thepresent invention;

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example ofcreating a learning experience in accordance with the present invention;

FIGS. 9A, 9B, 9C, and 9D are schematic block diagrams of an embodimentof a computing system illustrating an example of updating a lessonpackage in accordance with the present invention;

FIGS. 10A, 10B, and 10C are schematic block diagrams of an embodiment ofa computing system illustrating an example of selecting a lesson packagein accordance with the present invention;

FIGS. 11A, 11B, 11C, and 11D are schematic block diagrams of anembodiment of a computing system illustrating an example of utilizing alesson package in accordance with the present invention;

FIGS. 12A, 12B, and 12C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lesson packagein accordance with the present invention;

FIGS. 13A, 13B, and 13C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lesson packagein accordance with the present invention;

FIGS. 14A and 14B are schematic block diagrams of an embodiment of acomputing system illustrating an example of modifying a lesson packagein accordance with the present invention;

FIGS. 15A, 15B, and 15C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lesson packagein accordance with the present invention;

FIGS. 16A, 16B, and 16C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lesson packagein accordance with the present invention;

FIGS. 17A, 17B, and 17C are schematic block diagrams of an embodiment ofa computing system illustrating an example of selecting and utilizing alesson package in a virtual world environment in accordance with thepresent invention; and

FIGS. 18A, 18B, and 18C are schematic block diagrams of an embodiment ofa computing system illustrating an example of representing a lessonpackage in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a real world environment 12, an environmentsensor module 14, and environment model database 16, a human interfacemodule 18, and a computing entity 20. The real-world environment 12includes places 22, objects 24, instructors 26-1 through 26-N, andlearners 28-1 through 28-N. The computing entity 20 includes anexperience creation module 30, an experience execution module 32, and alearning assets database 34.

The places 22 includes any area. Examples of places 22 includes a room,an outdoor space, a neighborhood, a city, etc. The objects 24 includesthings within the places. Examples of objects 24 includes people,equipment, furniture, personal items, tools, and representations ofinformation (i.e., video recordings, audio recordings, captured text,etc.). The instructors includes any entity (e.g., human or human proxy)imparting knowledge. The learners includes entities trying to gainknowledge and may temporarily serve as an instructor.

In an example of operation of the computing system 10, the experiencecreation module 30 receives environment sensor information 38 from theenvironment sensor module 14 based on environment attributes 36 from thereal world environment 12. The environment sensor information 38includes time-based information (e.g., static snapshot, continuousstreaming) from environment attributes 36 including XYZ positioninformation, place information, and object information (i.e.,background, foreground, instructor, learner, etc.). The XYZ positioninformation includes portrayal in a world space industry standard format(e.g., with reference to an absolute position).

The environment attributes 36 includes detectable measures of thereal-world environment 12 to facilitate generation of amulti-dimensional (e.g., including time) representation of thereal-world environment 12 in a virtual reality and/or augmented realityenvironment. For example, the environment sensor module 14 producesenvironment sensor information 38 associated with a medical examinationroom and a subject human patient (e.g., an MRI). The environment sensormodule 14 is discussed in greater detail with reference to FIG. 4.

Having received the environment sensor information 38, the experiencecreation module 30 accesses the environment model database 16 to recovermodeled environment information 40. The modeled environment information40 includes a synthetic representation of numerous environments (e.g.,model places and objects). For example, the modeled environmentinformation 40 includes a 3-D representation of a typical humancirculatory system. The models include those that are associated withcertain licensing requirements (e.g., copyrights, etc.).

Having received the modeled environment information 40, the experiencecreation module 30 receives instructor information 44 from the humaninterface module 18, where the human interface module 18 receives humaninput/output (I/O) 42 from instructor 26-1. The instructor information44 includes a representation of an essence of communication with aparticipant instructor. The human I/O 42 includes detectable fundamentalforms of communication with humans or human proxies. The human interfacemodule 18 is discussed in greater detail with reference to FIG. 3.

Having received the instructor information 44, the experience creationmodule 30 interprets the instructor information 44 to identify aspectsof a learning experience. A learning experience includes numerousaspects of an encounter between one or more learners and an imparting ofknowledge within a representation of a learning environment thatincludes a place, multiple objects, and one or more instructors. Thelearning experience further includes an instruction portion (e.g., actsto impart knowledge) and an assessment portion (e.g., further actsand/or receiving of learner input) to determine a level of comprehensionof the knowledge by the one or more learners. The learning experiencestill further includes scoring of the level of comprehension andtallying multiple learning experiences to facilitate higher-levelcompetency accreditations (e.g., certificates, degrees, licenses,training credits, experiences completed successfully, etc.).

As an example of the interpreting of the instructor information 44, theexperience creation module 30 identifies a set of concepts that theinstructor desires to impart upon a learner and a set of comprehensionverifying questions and associated correct answers. The experiencecreation module 30 further identifies step-by-step instructorannotations associated with the various objects within the environmentof the learning experience for the instruction portion and theassessment portion. For example, the experience creation module 30identifies positions held by the instructor 26-1 as the instructornarrates a set of concepts associated with the subject patientcirculatory system. As a further example, the experience creation module30 identifies circulatory system questions and correct answers posed bythe instructor associated with the narrative.

Having interpreted the instructor information 44, the experiencecreation module 30 renders the environment sensor information 38, themodeled environment information 40, and the instructor information 44 toproduce learning assets information 48 for storage in the learningassets database 34. The learning assets information 48 includes allthings associated with the learning experience to facilitate subsequentrecreation. Examples includes the environment, places, objects,instructors, learners, assets, recorded instruction information,learning evaluation information, etc.

Execution of a learning experience for the one or more learners includesa variety of approaches. A first approach includes the experienceexecution module 32 recovering the learning assets information 48 fromthe learning assets database 34, rendering the learning experience aslearner information 46, and outputting the learner information 46 viathe human interface module 18 as further human I/O 42 to one or more ofthe learners 28-1 through 28-N. The learner information 46 includesinformation to be sent to the one or more learners and informationreceived from the one or more learners. For example, the experienceexecution module 32 outputs learner information 46 associated with theinstruction portion for the learner 28-1 and collects learnerinformation 46 from the learner 28-1 that includes submitted assessmentanswers in response to assessment questions of the assessment portioncommunicated as further learner information 46 for the learner 28-1.

A second approach includes the experience execution module 32 renderingthe learner information 46 as a combination of live streaming ofenvironment sensor information 38 from the real-world environment 12along with an augmented reality overlay based on recovered learningasset information 48. For example, a real world subject human patient ina medical examination room is live streamed as the environment sensorinformation 38 in combination with a prerecorded instruction portionfrom the instructor 26-1.

FIG. 2A is a schematic block diagram of an embodiment of the computingentity 20 of the computing system 10. The computing entity 20 includesone or more computing devices 100-1 through 100-N. A computing device isany electronic device that communicates data, processes data, representsdata (e.g., user interface) and/or stores data.

Computing devices include portable computing devices and fixed computingdevices. Examples of portable computing devices include an embeddedcontroller, a smart sensor, a social networking device, a gaming device,a smart phone, a laptop computer, a tablet computer, a video gamecontroller, and/or any other portable device that includes a computingcore. Examples of fixed computing devices includes a personal computer,a computer server, a cable set-top box, a fixed display device, anappliance, and industrial controller, a video game counsel, a homeentertainment controller, a critical infrastructure controller, and/orany type of home, office or cloud computing equipment that includes acomputing core.

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice 100 of the computing system 10 that includes one or morecomputing cores 52-1 through 52-N, a memory module 102, the humaninterface module 18, the environment sensor module 14, and an I/O module104. In alternative embodiments, the human interface module 18, theenvironment sensor module 14, the I/O module 104, and the memory module102 may be standalone (e.g., external to the computing device). Anembodiment of the computing device 100 will be discussed in greaterdetail with reference to FIG. 3.

FIG. 3 is a schematic block diagram of another embodiment of thecomputing device 100 of the computing system 10 that includes the humaninterface module 18, the environment sensor module 14, the computingcore 52-1, the memory module 102, and the I/O module 104. The humaninterface module 18 includes one or more visual output devices 74 (e.g.,video graphics display, 3-D viewer, touchscreen, LED, etc.), one or morevisual input devices 80 (e.g., a still image camera, a video camera, a3-D video camera, photocell, etc.), and one or more audio output devices78 (e.g., speaker(s), headphone jack, a motor, etc.). The humaninterface module 18 further includes one or more user input devices 76(e.g., keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.) and one or more motion output devices 106 (e.g., servos,motors, lifts, pumps, actuators, anything to get real-world objects tomove).

The computing core 52-1 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N, a memory controller 56, one ormore main memories 58-1 through 58-N (e.g., RAM), one or moreinput/output (I/O) device interface modules 62, an input/output (I/O)controller 60, and a peripheral interface 64. A processing module is asdefined at the end of the detailed description.

The memory module 102 includes a memory interface module 70 and one ormore memory devices, including flash memory devices 92, hard drive (HD)memory 94, solid state (SS) memory 96, and cloud memory 98. The cloudmemory 98 includes an on-line storage system and an on-line backupsystem.

The I/O module 104 includes a network interface module 72, a peripheraldevice interface module 68, and a universal serial bus (USB) interfacemodule 66. Each of the I/O device interface module 62, the peripheralinterface 64, the memory interface module 70, the network interfacemodule 72, the peripheral device interface module 68, and the USBinterface modules 66 includes a combination of hardware (e.g.,connectors, wiring, etc.) and operational instructions stored on memory(e.g., driver software) that are executed by one or more of theprocessing modules 50-1 through 50-N and/or a processing circuit withinthe particular module.

The I/O module 104 further includes one or more wireless location modems84 (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival,time difference of arrival, signal strength, dedicated wirelesslocation, etc.) and one or more wireless communication modems 86 (e.g.,a cellular network transceiver, a wireless data network transceiver, aWi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zigbee transceiver, a 60 GHz transceiver, etc.). The I/O module 104 furtherincludes a telco interface 108 (e.g., to interface to a public switchedtelephone network), a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical). The I/O module 104 further includes one or more peripheraldevices (e.g., peripheral devices 1-P) and one or more universal serialbus (USB) devices (USB devices 1-U). In other embodiments, the computingdevice 100 may include more or less devices and modules than shown inthis example embodiment.

FIG. 4 is a schematic block diagram of an embodiment of the environmentsensor module 14 of the computing system 10 that includes a sensorinterface module 120 to output environment sensor information 150 basedon information communicated with a set of sensors. The set of sensorsincludes a visual sensor 122 (e.g., to the camera, 3-D camera, 360° viewcamera, a camera array, an optical spectrometer, etc.) and an audiosensor 124 (e.g., a microphone, a microphone array). The set of sensorsfurther includes a motion sensor 126 (e.g., a solid-state Gyro, avibration detector, a laser motion detector) and a position sensor 128(e.g., a Hall effect sensor, an image detector, a GPS receiver, a radarsystem).

The set of sensors further includes a scanning sensor 130 (e.g., CATscan, Mill, x-ray, ultrasound, radio scatter, particle detector, lasermeasure, further radar) and a temperature sensor 132 (e.g., thermometer,thermal coupler). The set of sensors further includes a humidity sensor134 (resistance based, capacitance based) and an altitude sensor 136(e.g., pressure based, GPS-based, laser-based).

The set of sensors further includes a biosensor 138 (e.g., enzyme,immuno, microbial) and a chemical sensor 140 (e.g., mass spectrometer,gas, polymer). The set of sensors further includes a magnetic sensor 142(e.g., Hall effect, piezo electric, coil, magnetic tunnel junction) andany generic sensor 144 (e.g., including a hybrid combination of two ormore of the other sensors).

FIG. 5A is a schematic block diagram of another embodiment of acomputing system that includes the environment model database 16, thehuman interface module 18, the instructor 26-1, the experience creationmodule 30, and the learning assets database 34 of FIG. 1. In an exampleof operation, the experience creation module 30 obtains modeledenvironment information 40 from the environment model database 16 andrenders a representation of an environment and objects of the modeledenvironment information 40 to output as instructor output information160. The human interface module 18 transforms the instructor outputinformation 160 into human output 162 for presentation to the instructor26-1. For example, the human output 162 includes a 3-D visualization andstereo audio output.

In response to the human output 162, the human interface module 18receives human input 164 from the instructor 26-1. For example, thehuman input 164 includes pointer movement information and human speechassociated with a lesson. The human interface module 18 transforms thehuman input 164 into instructor input information 166. The instructorinput information 166 includes one or more of representations ofinstructor interactions with objects within the environment and explicitevaluation information (e.g., questions to test for comprehension level,and correct answers to the questions).

Having received the instructor input information 166, the experiencecreation module 30 renders a representation of the instructor inputinformation 166 within the environment utilizing the objects of themodeled environment information 40 to produce learning asset information48 for storage in the learnings assets database 34. Subsequent access ofthe learning assets information 48 facilitates a learning experience.

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience that includes a virtual place168 and a resulting learning objective 170. A learning objectiverepresents a portion of an overall learning experience, where thelearning objective is associated with at least one major concept ofknowledge to be imparted to a learner. The major concept may includeseveral sub-concepts. The makeup of the learning objective is discussedin greater detail with reference to FIG. 6.

The virtual place 168 includes a representation of an environment (e.g.,a place) over a series of time intervals (e.g., time 0-N). Theenvironment includes a plurality of objects 24-1 through 24-N. At eachtime reference, the positions of the objects can change in accordancewith the learning experience. For example, the instructor 26-1 of FIG.5A interacts with the objects to convey a concept. The sum of thepositions of the environment and objects within the virtual place 168 iswrapped into the learning objective 170 for storage and subsequentutilization when executing the learning experience.

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes a plurality ofmodules 1-N. Each module includes a set of lessons 1-N. Each lessonincludes a plurality of learning objectives 1-N. The learning experiencetypically is played from left to right where learning objectives aresequentially executed in lesson 1 of module 1 followed by learningobjectives of lesson 2 of module 1 etc.

As learners access the learning experience during execution, theordering may be accessed in different ways to suit the needs of theunique learner based on one or more of preferences, experience,previously demonstrated comprehension levels, etc. For example, aparticular learner may skip over lesson 1 of module 1 and go right tolesson 2 of module 1 when having previously demonstrated competency ofthe concepts associated with lesson 1.

Each learning objective includes indexing information, environmentinformation, asset information, instructor interaction information, andassessment information. The index information includes one or more ofcategorization information, topics list, instructor identification,author identification, identification of copyrighted materials,keywords, concept titles, prerequisites for access, and links to relatedlearning objectives.

The environment information includes one or more of structureinformation, environment model information, background information,identifiers of places, and categories of environments. The assetinformation includes one or more of object identifiers, objectinformation (e.g., modeling information), asset ownership information,asset type descriptors (e.g., 2-D, 3-D). Examples include models ofphysical objects, stored media such as videos, scans, images, digitalrepresentations of text, digital audio, and graphics.

The instructor interaction information includes representations ofinstructor annotations, actions, motions, gestures, expressions, eyemovement information, facial expression information, speech, and speechinflections. The content associated with the instructor interactioninformation includes overview information, speaker notes, actionsassociated with assessment information, (e.g., pointing to questions,revealing answers to the questions, motioning related to posingquestions) and conditional learning objective execution orderinginformation (e.g., if the learner does this then take this path,otherwise take another path).

The assessment information includes a summary of desired knowledge toimpart, specific questions for a learner, correct answers to thespecific questions, multiple-choice question sets, and scoringinformation associated with writing answers. The assessment informationfurther includes historical interactions by other learners with thelearning objective (e.g., where did previous learners look most oftenwithin the environment of the learning objective, etc.), historicalresponses to previous comprehension evaluations, and actions tofacilitate when a learner responds with a correct or incorrect answer(e.g., motion stimulus to activate upon an incorrect answer to increasea human stress level).

FIG. 7A is a schematic block diagram of another embodiment of acomputing system that includes the learning assets database 34, theexperience execution module 32, the human interface module 18, and thelearner 28-1 of FIG. 1. In an example of operation, the experienceexecution module 32 recovers learning asset information 48 from thelearning assets database 34 (e.g., in accordance with a selection by thelearner 28-1). The experience execution module 32 renders a group oflearning objectives associated with a common lesson within anenvironment utilizing objects associated with the lesson to producelearner output information 172. The learner output information 172includes a representation of a virtual place and objects that includesinstructor interactions and learner interactions from a perspective ofthe learner.

The human interface module 18 transforms the learner output information172 into human output 162 for conveyance of the learner outputinformation 172 to the learner 28-1. For example, the human interfacemodule 18 facilitates displaying a 3-D image of the virtual environmentto the learner 28-1.

The human interface module 18 transforms human input 164 from thelearner 28-1 to produce learner input information 174. The learner inputinformation 174 includes representations of learner interactions withobjects within the virtual place (e.g., answering comprehension levelevaluation questions).

The experience execution module 32 updates the representation of thevirtual place by modifying the learner output information 172 based onthe learner input information 174 so that the learner 28-1 enjoysrepresentations of interactions caused by the learner within the virtualenvironment. The experience execution module 32 evaluates the learnerinput information 174 with regards to evaluation information of thelearning objectives to evaluate a comprehension level by the learner28-1 with regards to the set of learning objectives of the lesson.

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes the learningobjective 170 and the virtual place 168. In an example of operation, thelearning objective 170 is recovered from the learning assets database 34of FIG. 7A and rendered to create the virtual place 168 representationsof objects 24-1 through 24-N in the environment from time referenceszero through N. For example, a first object is the instructor 26-1 ofFIG. 5A, a second object is the learner 28-1 of FIG. 7A, and theremaining objects are associated with the learning objectives of thelesson, where the objects are manipulated in accordance with annotationsof instructions provided by the instructor 26-1.

The learner 28-1 experiences a unique viewpoint of the environment andgains knowledge from accessing (e.g., playing) the learning experience.The learner 28-1 further manipulates objects within the environment tosupport learning and assessment of comprehension of objectives of thelearning experience.

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience. The computing system includes the environment model database16, the experience creation module 30, and the learning assets database34 of FIG. 1. The experience creation module 30 includes a learning pathmodule 180, an asset module 182, an instruction module 184, and a lessongeneration module 186.

In an example of operation, FIG. 8A illustrates the learning path module180 determining a learning path (e.g., structure and ordering oflearning objectives to complete towards a goal such as a certificate ordegree) to include multiple modules and/or lessons. For example, thelearning path module 180 obtains learning path information 194 from thelearning assets database 34 and receives learning path structureinformation 190 and learning objective information 192 (e.g., from aninstructor) to generate updated learning path information 196.

The learning path structure information 190 includes attributes of thelearning path and the learning objective information 192 includes asummary of desired knowledge to impart. The updated learning pathinformation 196 is generated to include modifications to the learningpath information 194 in accordance with the learning path structureinformation 190 in the learning objective information 192.

The asset module 182 determines a collection of common assets for eachlesson of the learning path. For example, the asset module 182 receivessupporting asset information 198 (e.g., representation information ofobjects in the virtual space) and modeled asset information 200 from theenvironment model database 16 to produce lesson asset information 202.The modeled asset information 200 includes representations of anenvironment to support the updated learning path information 196 (e.g.,modeled places and modeled objects) and the lesson asset information 202includes a representation of the environment, learning path, theobjectives, and the desired knowledge to impart.

FIG. 8B further illustrates the example of operation where theinstruction module 184 outputs a representation of the lesson assetinformation 202 as instructor output information 160. The instructoroutput information 160 includes a representation of the environment andthe asset so far to be experienced by an instructor who is about toinput interactions with the environment to impart the desired knowledge.

The instruction module 184 receives instructor input information 166from the instructor in response to the instructor output information160. The instructor input information 166 includes interactions from theinstructor to facilitate imparting of the knowledge (e.g., instructorannotations, pointer movements, highlighting, text notes, and speech)and testing of comprehension of the knowledge (e.g., valuationinformation such as questions and correct answers). The instructionmodule 184 obtains assessment information (e.g., comprehension testpoints, questions, correct answers to the questions) for each learningobjective based on the lesson asset information 202 and producesinstruction information 204 (e.g., representation of instructorinteractions with objects within the virtual place, evaluationinformation).

FIG. 8C further illustrates the example of operation where the lessongeneration module 186 renders (e.g., as a multidimensionalrepresentation) the objects associated with each lesson (e.g., assets ofthe environment) within the environment in accordance with theinstructor interactions for the instruction portion and the assessmentportion of the learning experience. Each object is assigned a relativeposition in XYZ world space within the environment to produce the lessonrendering.

The lesson generation module 186 outputs the rendering as a lessonpackage 206 for storage in the learning assets database 34. The lessonpackage 206 includes everything required to replay the lesson for asubsequent learner (e.g., representation of the environment, theobjects, the interactions of the instructor during both the instructionand evaluation portions, questions to test comprehension, correctanswers to the questions, a scoring approach for evaluatingcomprehension, all of the learning objective information associated witheach learning objective of the lesson).

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system (e.g., the computingsystem 10 of FIG. 1). In particular, a method is presented inconjunction with one or more functions and features described inconjunction with FIGS. 1-7B, and also FIGS. 8A-8C. The method includesstep 220 where a processing module of one or more processing modules ofone or more computing devices within the computing system determinesupdated learning path information based on learning path information,learning path structure information, and learning objective information.For example, the processing module combines a previous learning pathwith obtained learning path structure information in accordance withlearning objective information to produce the updated learning pathinformation (i.e., specifics for a series of learning objectives of alesson).

The method continues at step 222 where the processing module determineslesson asset information based on the updated learning path information,supporting asset information, and modeled asset information. Forexample, the processing module combines assets of the supporting assetinformation (e.g., received from an instructor) with assets and a placeof the modeled asset information in accordance with the updated learningpath information to produce the lesson asset information. The processingmodule selects assets as appropriate for each learning objective (e.g.,to facilitate the imparting of knowledge based on a predeterminationand/or historical results).

The method continues at step 224 where the processing module obtainsinstructor input information. For example, the processing module outputsa representation of the lesson asset information as instructor outputinformation and captures instructor input information for each lesson inresponse to the instructor output information. Further obtain assetinformation for each learning objective (e.g., extract from theinstructor input information).

The method continues at step 226 where the processing module generatesinstruction information based on the instructor input information. Forexample, the processing module combines instructor gestures and furtherenvironment manipulations based on the assessment information to producethe instruction information.

The method continues at step 228 where the processing module renders,for each lesson, a multidimensional representation of environment andobjects of the lesson asset information utilizing the instructioninformation to produce a lesson package. For example, the processingmodule generates the multidimensional representation of the environmentthat includes the objects and the instructor interactions of theinstruction information to produce the lesson package. For instance, theprocessing module includes a 3-D rendering of a place, backgroundobjects, recorded objects, and the instructor in a relative position XYZworld space over time.

The method continues at step 230 where the processing module facilitatesstorage of the lesson package. For example, the processing moduleindexes the one or more lesson packages of the one or more lessons ofthe learning path to produce indexing information (e.g., title, author,instructor identifier, topic area, etc.). The processing module storesthe indexed lesson package as learning asset information in a learningassets database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example of amethod to create a learning experience. The embodiment includes creatinga multi-disciplined learning tool regarding a topic. Themulti-disciplined aspect of the learning tool includes both disciplinesof learning and any form/format of presentation of content regarding thetopic. For example, a first discipline includes mechanical systems, asecond discipline includes electrical systems, and a third disciplineincludes fluid systems when the topic includes operation of a combustionbased engine. The computing system includes the environment modeldatabase 16 of FIG. 1, the learning assets database 34 of FIG. 1, andthe experience creation module 30 of FIG. 1.

FIG. 8E illustrates the example of operation where the experiencecreation module 30 creates a first-pass of a first learning object 700-1for a first piece of information regarding the topic to include a firstset of knowledge bullet-points 702-1 regarding the first piece ofinformation. The creating includes utilizing guidance from an instructorand/or reusing previous knowledge bullet-points for a related topic. Forexample, the experience creation module 30 extracts the bullet-pointsfrom one or more of learning path structure information 190 and learningobjective information 192 when utilizing the guidance from theinstructor. As another example, the experience creation module 30extracts the bullet-points from learning path information 194 retrievedfrom the learning assets database 34 when utilizing previous knowledgebullet-points for the related topic.

Each piece of information is to impart additional knowledge related tothe topic. The additional knowledge of the piece of information includesa characterization of learnable material by most learners in just a fewminutes. As a specific example, the first piece of information includes“4 cycle engine intake cycles” when the topic includes “how a 4 cycleengine works.”

Each of the knowledge bullet-points are to impart knowledge associatedwith the associated piece of information in a logical (e.g., sequential)and knowledge building fashion. As a specific example, the experiencecreation module 30 creates the first set of knowledge bullet-points702-1 based on instructor input to include a first bullet-point “intakestroke: intake valve opens, air/fuel mixture pulled into cylinder bypiston” and a second bullet-point “compression stroke: intake valvecloses, piston compresses air/fuel mixture in cylinder” when the firstpiece of information includes the “4 cycle engine intake cycles.”

FIG. 8F further illustrates the example of operation where theexperience creation module 30 creates a first-pass of a second learningobject 700-2 for a second piece of information regarding the topic toinclude a second set of knowledge bullet-points 702-2 regarding thesecond piece of information. As a specific example, the experiencecreation module 30 creates the second set of knowledge bullet-points702-2 based on the instructor input to include a first bullet-point“power stroke: spark plug ignites air/fuel mixture pushing piston” and asecond bullet-point “exhaust stroke: exhaust valve opens and pistonpushes exhaust out of cylinder, exhaust valve closes” when the secondpiece of information includes “4 cycle engine outtake cycles.”

FIG. 8G further illustrates the example of operation where theexperience creation module 30 obtains illustrative assets 704 based onthe first and second set of knowledge bullet-points 702-1 and 702-2. Theillustrative assets 704 depicts one or more aspects regarding the topicpertaining to the first and second pieces of information. Examples ofillustrative assets includes background environments, objects within theenvironment (e.g., things, tools), where the objects and the environmentare represented by multidimensional models (e.g., 3-D model) utilizing avariety of representation formats including video, scans, images, text,audio, graphics etc.

The obtaining of the illustrative assets 704 includes a variety ofapproaches. A first approach includes interpreting instructor inputinformation to identify the illustrative asset. For example, theexperience creation module 30 interprets instructor input information toidentify a cylinder asset.

A second approach includes identifying a first object of the first andsecond set of knowledge bullet-points as an illustrative asset. Forexample, the experience creation module 30 identifies the piston objectfrom both the first and second set of knowledge bullet-points.

A third approach includes determining the illustrative assets 704 basedon the first object of the first and second set of knowledgebullet-points. For example, the experience creation module 30 accessesthe environment model database 16 to extract information about an assetfrom one or more of supporting asset information 198 and modeled assetinformation 200 for a sparkplug when interpreting the first and secondset of knowledge bullet-points.

FIG. 8H further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the firstlearning object 700-1 to further include first descriptive assets 706-1regarding the first piece of information based on the first set ofknowledge bullet-points 702-1 and the illustrative assets 704.Descriptive assets include instruction information that utilizes theillustrative asset 704 to impart knowledge and subsequently test forknowledge retention. The embodiments of the descriptive assets includesmultiple disciplines and multiple dimensions to provide improvedlearning by utilizing multiple senses of a learner. Examples of theinstruction information includes annotations, actions, motions,gestures, expressions, recorded speech, speech inflection information,review information, speaker notes, and assessment information.

The creating the second-pass of the first learning object 700-1 includesgenerating a representation of the illustrative assets 704 based on afirst knowledge bullet-point of the first set of knowledge bullet-points702-1. For example, the experience creation module 30 renders 3-D framesof a 3-D model of the cylinder, the piston, the spark plug, the intakevalve, and the exhaust valve in motion when performing the intake strokewhere the intake valve opens and the air/fuel mixture is pulled into thecylinder by the piston.

The creating of the second-pass of the first learning object 700-1further includes generating the first descriptive assets 706-1 utilizingthe representation of the illustrative assets 704. For example, theexperience creation module 30 renders 3-D frames of the 3-D models ofthe various engine parts without necessarily illustrating the first setof knowledge bullet-points 702-1.

In an embodiment where the experience creation module 30 generates therepresentation of the illustrative assets 704, the experience creationmodule 30 outputs the representation of the illustrative asset 704 asinstructor output information 160 to an instructor. For example, the 3-Dmodel of the cylinder and associated parts.

The experience creation module 30 receives instructor input information166 in response to the instructor output information 160. For example,the instructor input information 166 includes instructor annotations tohelp explain the intake stroke (e.g., instructor speech, instructorpointer motions). The experience creation module 30 interprets theinstructor input information 166 to produce the first descriptive assets706-1. For example, the renderings of the engine parts include theintake stroke as annotated by the instructor.

FIG. 8J further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the secondlearning object 700-2 to further include second descriptive assets 706-2regarding the second piece of information based on the second set ofknowledge bullet-points 702-2 and the illustrative assets 704. Forexample, the experience creation module 30 creates 3-D renderings of thepower stroke and the exhaust stroke as annotated by the instructor basedon further instructor input information 166.

FIG. 8K further illustrates the example of operation where theexperience creation module 30 links the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form at least aportion of the multi-disciplined learning tool. For example, theexperience creation module 30 aggregates the first learning object 700-1and the second learning object 700-2 to produce a lesson package 206 forstorage in the learning assets database 34.

In an embodiment, the linking of the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form the at leastthe portion of the multi-disciplined learning tool includes generatingindex information for the second-passes of first and second learningobjects to indicate sharing of the illustrative asset 704. For example,the experience creation module 30 generates the index information toidentify the first learning object 700-1 and the second learning object700-2 as related to the same topic.

The linking further includes facilitating storage of the indexinformation and the first and second learning objects 700-1 and 700-2 inthe learning assets database 34 to enable subsequent utilization of themulti-disciplined learning tool. For example, the experience creationmodule 30 aggregates the first learning object 700-1, the secondlearning object 700-2, and the index information to produce the lessonpackage 206 for storage in the learning assets database 34.

The method described above with reference to FIGS. 8E-8K in conjunctionwith the experience creation module 30 can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devicesincluding various embodiments of the computing entity 20 of FIG. 2A. Inaddition, at least one memory section (e.g., a computer readable memory,a non-transitory computer readable storage medium, a non-transitorycomputer readable memory organized into a first memory element, a secondmemory element, a third memory element, a fourth element section, afifth memory element, a sixth memory element, etc.) that storesoperational instructions can, when executed by one or more processingmodules of the one or more computing entities of the computing system10, cause boy one or more computing devices to perform any or all of themethod steps described above.

FIGS. 9A, 9B, 9C, 9D, and 9E are schematic block diagrams of anembodiment of a computing system illustrating an example of updating alesson package. The computing system includes the environment sensormodule 14 of FIG. 1, the experience creation module 30 of FIG. 1, thelearning assets database 34 of FIG. 1, and the experience executionmodule 32 of FIG. 1. In an embodiment, the environment sensor module 14includes the motion sensor 126 of FIG. 4 and the position sensor 128 ofFIG. 4. The experience creation module 30 includes the lesson generationmodule 186 of FIG. 8A. The experience execution module 32 includes anenvironment generation module 240, an instance experience module 290,and a learning assessment module 330.

FIG. 9A illustrates an example of a method of operation to update thelesson package where, in a first step the experience execution module 32issues a representation of a first set of physicality assessment assetsof a first learning object of a plurality of learning objects to asecond computing entity. For example, the environment generation module240 generates instruction information 204 and baseline environment andobject information 292 based on a lesson package 206 recovered from thelearning assets database 34. The lesson package 206 includes theplurality of learning objects.

The instruction information 204 includes a representation of instructorinteractions with objects within the virtual environment and evaluationinformation. The baseline environment and object information 292includes XYZ positioning information of each object within theenvironment for the lesson package 206. The instance experience module290 generates learner output information 172 for a first portion of thelesson package based on a learner profile, the instruction information204 and the baseline environment and object information 292.

The plurality of learning objects includes the first learning object anda second learning object. The first learning object includes a first setof knowledge bullet-points for a first piece of information regarding atopic. The second learning object includes a second set of knowledgebullet-points for a second piece of information regarding the topic.

The first learning object and the second learning object further includean illustrative asset that depicts an aspect regarding the topicpertaining to the first and the second pieces of information. The firstlearning object further includes at least one first descriptive assetregarding the first piece of information based on the first set ofknowledge bullet-points and the illustrative asset. The second learningobject further includes at least one second descriptive asset regardingthe second piece of information based on the second set of knowledgebullet-points and the illustrative asset.

The issuing of the representation of the first learning object furtherincludes the instance experience module 290 generating the firstdescriptive asset for the first learning object utilizing the first setof knowledge bullet-points and the illustrative asset as previouslydiscussed. The instance experience module 290 outputs a representationof the first descriptive asset to a computing entity associated with alearner 28-1. For example, the instance experience module 290 rendersthe first descriptive asset to produce a rendering and issues therendering as learner output information 172 to a second computing entity(e.g., associated with the learner 28-1) as a representation of thefirst learning object.

The issuing of the representation of the first learning object furtherincludes the instance experience module 290 issuing the representationof the first set of physicality assessment assets of the first learningobject to the second computing entity (e.g., associated with the learner28-1). The issuing of the representation of the first set of physicalityassessment assets further includes a series of sub-steps.

A first sub-step includes deriving a first set of knowledge test-pointsfor the first learning object regarding the topic based on the first setof knowledge bullet-points, where a first knowledge test-point of thefirst set of knowledge test-points includes a physicality aspect. Thephysicality aspect includes at least one of performance of a physicalactivity to demonstrate command of a knowledge test-point and answeringa question during physical activity to demonstrate cognitive functionduring physical activity. For instance, the instance experience module290 generates the first knowledge test-point to include performingcardiopulmonary resuscitation (CPR) when the first set of knowledgebullet-points pertain to aspects of successful CPR.

A second sub-step includes generating the first set of physicalityassessment assets utilizing the first set of knowledge test-points, theillustrative asset, and the first descriptive asset of the firstlearning object. For instance, the instance experience module 290generates the first set of physicality assessment assets to include aCPR test device and an instruction to perform CPR.

A third sub-step of the issuing of the representation of the first setof physicality assessment assets includes rendering the first set ofphysicality assessment assets to produce the representation of the firstset of physicality assessment assets. For instance, the instanceexperience module 290 renders the first set of physicality assessmentassets to produce a rendering as the representation.

A fourth sub-step includes outputting the representation of the firstset of physicality assessment assets to the second computing entityassociated with the learner 28-1. For instance, the instance experiencemodule 290 outputs learner output information 172 that includes therendering of the first set of physicality assessment assets.

FIG. 9B further illustrates the example of operation of the method toupdate the lesson package, where, having issued the representation ofthe first set of physicality assessment assets, in a second step of themethod the experience execution module 32 obtains a first assessmentresponse in response to the representation of the first set ofphysicality assessment assets. The obtaining of the first assessmentresponse includes a variety of approaches.

A first approach includes receiving the first assessment response fromthe second computing entity in response to the representation of thefirst set of physicality assessment assets. For example, the instanceexperience module 290 receives learner input information 174 andextracts the first assessment response from the received learner inputinformation 174.

A second approach includes receiving the first assessment response froma third computing entity. For example, the instance experience modulereceives the first assessment response from a computing entityassociated with monitoring physicality aspects of the learner 28-1.

A third approach includes interpreting learner interaction information332 to produce the first assessment response. For example, the instanceexperience module 290 interprets the learner input information 174 basedon assessment information 252 to produce the learner interactioninformation 332. For instance, the assessment information 252 includeshow to assess the learner input information 174 to produce the learnerinteraction information 332. The learning assessment module 330interprets the learner interaction information 332 based on theassessment information 252 to produce learning assessment resultsinformation 334 as the first assessment response.

A fourth approach includes interpreting environment sensor information150 to produce the first assessment response. For example, the learningassessment module 330 interprets the environment sensor information 150from the environment sensor module 14 with regards to detecting physicalmanipulations of the CPR test device (e.g., as detected by the motionsensor 126 and/or the position sensor 128) to produce the firstassessment response.

FIG. 9C further illustrates the example of operation of the method toupdate the lesson package where, having obtained the first assessmentresponse, in a third step the experience execution module 32 determinesan undesired performance aspect of the first assessment response. Thedetermining the undesired performance aspect of the first assessmentresponse includes a series of steps. A first step includes evaluatingthe first assessment response utilizing evaluation criteria of theassessment information 252 to produce a first assessment responseevaluation. The evaluation criteria includes measures to assist indetermining performance of the learner 28-1 (e.g., rate of performingCPR, compression depths of the CPR, etc.) The learning assessment module330 evaluates the learner interaction information 332 and theenvironment sensor information 150 utilizing the evaluation criteria ofthe assessment information 252 to produce learning assessment resultsinformation 334. For example, the learning assessment module 330analyzes the environment sensor information 150 to interpret physicalactions of the learner 28-1 to determine the rate of performing the CPRand the compression depths of the CPR.

The learning assessment results information 334 includes one or more ofa learner identity, a learning object identifier, a lesson identifier,evaluation criteria, an undesired performance aspect, and assemblyerror, a disassembly error, and raw learner interaction information(e.g., a timestamp recording of all learner interactions like points,speech, input text, settings, viewpoints, etc.). The learning assessmentresults information 334 further includes summarized learner interactioninformation (e.g., average, mins, maxes of raw interaction information,time spent looking at each view of a learning object, how fast answersare provided, number of wrong answers, number of right answers,comparisons of measures to desired values of the evaluation criteria,etc.).

A second step includes identifying the undesired performance aspect ofthe first assessment response based on the first assessment responseevaluation and evaluation criteria of the assessment information. Theevaluation criteria includes desired ranges of the measures, e.g.,greater than a minimum value, less than a maximum value, between theminimum and maximum values, etc. For example, the learning assessmentmodule 330 compares the rate of performing the CPR to a desired CPR raterange measure and indicates that the CPR range is the undesiredperformance aspect when the rate of performing the CPR is outside of thedesired CPR rate range.

FIG. 9D further illustrates the example of operation of the method toupdate the lesson package where, having determined the undesiredperformance aspect of the first assessment response, in a fourth step,the experience creation module 30 updates at least one of the firstlearning object and the second learning object based on the undesiredperformance aspect to facilitate improved performance of a subsequentassessment response. The updating of the at least one of the firstlearning object and the second learning object includes a variety ofapproaches.

A first approach includes the lesson generation module 186 modifying thefirst descriptive asset regarding the first piece of information basedon the undesired performance aspect, the first set of knowledgebullet-points, and the illustrative asset. For example, the lessongeneration module 186 extracts the first descriptive asset from thelesson package 206, extracts the first set of knowledge bullet-pointsfrom the lesson package 206, extracts the illustrative asset from thelesson package 206, and extracts the undesired performance aspect fromthe learning assessment results information 334.

The first approach further includes the lesson generation module 186determining a modification approach based on the undesired performanceaspect. For example, the lesson generation module 186 determines tomodify the first descriptive asset when the undesired performance aspectis associated with potential performance improvement for the firstlearning object.

As an instance of the modification to the first learning object, whenunfavorable motion of the learner 28-1 related to an object occurs morethan a maximum unfavorable threshold level (e.g., too muchunderperforming), the lesson generation module 186 determines themodification to the first descriptive asset (e.g., new version,different view, take more time viewing the object, etc.). As anotherexample, when favorable motion of the learner 28-1 related to the objectoccurs more than a maximum unfavorable threshold level (e.g., too muchoutperforming), the lesson generation module 186 determines to furthermodify the first descriptive asset (e.g., new simple version, differentview, take less time viewing the object, etc.).

A second approach includes the lesson generation module 186 modifyingthe second descriptive asset regarding the second piece of informationbased on the undesired performance aspect, the second set of knowledgebullet-points, and the illustrative asset. For example, the lessongeneration module 186 extracts the second descriptive asset from thelesson package 206, extracts the second set of knowledge bullet-pointsfrom the lesson package 206, extracts the illustrative asset from thelesson package 206, and extracts the undesired performance aspect fromthe learning assessment results information 334.

The second approach further includes the lesson generation module 186determining the modification approach based on the undesired performanceaspect. For example, the lesson generation module 186 determines tomodify the second descriptive asset when the undesired performanceaspect is associated with potential performance improvement for thesecond learning object. For example, the lesson generation module 186determines to modify the second descriptive asset when the undesiredperformance aspect is associated with potential performance improvementfor the second learning object.

As an instance of the modification to the second learning object, whenunfavorable motion of the learner 28-1 related to an object occurs morethan a maximum unfavorable threshold level (e.g., too muchunderperforming), the lesson generation module 186 determines themodification to the second descriptive asset (e.g., new version,different view, take more time viewing the object, etc.). As anotherexample, when favorable motion of the learner 28-1 related to the objectoccurs more than a maximum unfavorable threshold level (e.g., too muchoutperforming), the lesson generation module 186 determines to furthermodify the second descriptive asset (e.g., new simple version, differentview, take less time viewing the object, etc.).

Alternatively, or in addition to, for each learning object of the lessonpackage 206, the experience creation module 30 identifies enhancementsto descriptive assets and/or their use to produce updated descriptiveassets of an updated lesson package 810 based on the correspondinglearning assessment results information 334. Having produced the updatedlesson package 810, the lesson generation module 186 facilitates storingthe updated lesson package 810 in the learning assets database 34 tofacilitate subsequent utilization of the updated lesson package 810 byanother learner to produce more favorable learning results.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 10A, 10B, and 10C are schematic block diagrams of an embodiment ofa computing system illustrating an example of selecting a lessonpackage. The computing system includes the environment sensor module 14of FIG. 1, the experience execution module 32 of FIG. 1, and thelearning assets database 34 of FIG. 1. In an embodiment, the environmentsensor module 14 includes the motion sensor 126, the position sensor128, the visual sensor 122, and the audio sensor 124, all of FIG. 4. Theexperience execution module 32 includes the environment generationmodule 240 of FIG. 9A and the instance experience module 290 of FIG. 9A.

FIG. 10A illustrates an example of a method of operation to select thelesson package where, in a first step the experience execution module 32interprets environment sensor information 150 to identify an environmentobject associated with a plurality of learning objects. The plurality oflearning objects are associated with the learning assets database 34.

A first learning object of the plurality of learning objects includes afirst set of knowledge bullet-points for a first piece of informationregarding a topic. A second learning object of the plurality of learningobjects includes a second set of knowledge bullet-points for a secondpiece of information regarding the same topic. The first learning objectand the second learning object further include an illustrative assetthat depicts an aspect regarding the topic pertaining to the first andthe second pieces of information. The first learning object furtherincludes a first descriptive asset regarding the first piece ofinformation based on the first set of knowledge bullet-points and theillustrative asset. The second learning object further includes a seconddescriptive asset regarding the second piece of information based on thesecond set of knowledge bullet-points and the illustrative asset.

The interpreting the environment sensor information to identify theenvironment object associated with the plurality of learning objectsincludes a variety of approaches. A first approach includes matching animage of the environment sensor information to an image associated withthe environment object. For example, the environment generation module240 matches an image of the environment sensor information 150 to animage associated with the object 24-1 of a lesson package 206 (e.g.,including one or more learning objects 880-1 through 880-N and/orlearning objects 882-1 through 882-N) from the learning assets database34.

A second approach include matching an alarm code of the environmentsensor information to an alarm code associated with the environmentobject. For example, the environment generation module 240 matches thealarm code from the object 24-1 via the environment sensor information150 to an alarm code associated with the object 24-1 of the lessonpackage 206.

A third approach includes matching a sound of the environment sensorinformation to a sound associated with the environment object. Forexample, the environment generation module 240 matches a portion of asound file from the object 24-1 via the environment sensor information150 to a sound file associated with the object 24-1 of the lessonpackage 206.

A fourth approach includes matching an identifier of the environmentsensor information to an identifier associated with the environmentobject. For example, the environment generation module 240 matches anidentifier extracted from the environment sensor information 150 to anidentifier associated with the object 24-1 of the lesson package 206.

FIG. 10B further illustrates the example of the method of operation toselect the lesson package, where having identified the environmentobject, in a second step the experience execution module 32 detects animpairment associated with the environment object. The impairmentincludes any unfavorable condition associated with the environmentobject. Examples of impairments include an engine error code, alarm, amanagement system message depicting an error condition, a visualassociated with a broken component, a sound associated with a worseningcondition, an image associated with improper usage, an indication ofimproper installation and/or maintenance, etc.

The detecting the impairment associated with the environment objectincludes a variety of approaches. A first approach includes determininga service requirement for the environment object. For example, theenvironment generation module 240 determines compares a service scheduleto service records to produce the service requirement for the object24-1.

A second approach includes determining a maintenance requirement for theenvironment object. For example, the environment generation module 240compares a maintenance schedule to maintenance records to produce themaintenance requirement for the object 24-1.

A third approach includes matching an image of the environment sensorinformation to an image associated with the impairment associated withthe environment object. For example, the environment generation module240 interprets the environment sensor information 150 to produce animage of a broken component of the object 24-1 and compares the image ofthe broken component to an image associated with the impairment.

A fourth approach includes matching an alarm code of the environmentsensor information to an alarm code associated with the impairmentassociated with the environment object. For example, the environmentgeneration module 240 extracts the alarm code from the environmentsensor information 150 and matches the extracted alarm code to an alarmcode associated with the impairment for the object 24-1. For instance,the environment generation module 240 matches an engine error code fromthe object 24-1 to a valid engine error code of a set of engine errorcodes associated with the object 24-1 depicted in one or more of theplurality of learning objects.

A fifth approach includes matching a sound of the environment sensorinformation to a sound associated with the impairment associated withthe environment object. For example, the environment generation module240 extracts the sound from the environment sensor information 150 andmatches the extracted sound to a sound file associated with theimpairment for the object 24-1.

A sixth approach includes matching an identifier of the environmentsensor information to an identifier associated with the impairmentassociated with the environment object. For example, the environmentgeneration module 240 extracts the identifier from the environmentsensor information 150 and compares the extracted identifier to theidentifier associated with impairment for the object 24-1.

Having detected the impairment, a third step of the example method ofoperation to select the lesson package includes the experience executionmodule 32 selecting the first learning object and the second learningobject when the first learning object and the second learning objectpertain to the impairment. The selecting includes selecting learningobjects for the environment object and then of those selected learningobjects down select learning objects associated with the detectedimpairment. For example, the environment generation module 240 comparesthe object 24-1 to objects of learning objects 880-1 through 880-N andof learning objects 882-1 through 882-N, etc. and selects the group oflearning objects 880-1 through 880-N when the comparison is favorable.Having selected the learning objects associated with the environmentobject, the environment generation module 240 selects learning objects880-1 and 880-2 when those first and second learning objects areassociated with the detected impairment (e.g., an engine error code).

Having selected the first and second learning objects, a fourth step ofthe example method of operation to select the lesson package includesthe experience execution module 32 rendering a portion of theillustrative asset to produce a set of illustrative asset video frames.For example, the environment generation module 240 renders theillustrative asset 705 to produce illustrative asset video frames 400.For instance, the environment generation module 240 renders depictionsof engine components common to both the learning object 880-1 and thelearning object 880-2 to produce the illustrative asset video frames400.

Having produced the set of illustrative asset video frames, a fifth stepof the example method of operation to select the lesson package includesexperience execution module 32 selecting a common subset of the set ofillustrative asset video frames to produce a first portion of firstdescriptive asset video frames of the first descriptive asset and toproduce a first portion of second descriptive asset video frames of thesecond descriptive asset, so that subsequent utilization of the commonsubset of the set of illustrative asset video frames reduces renderingof other first and second descriptive asset video frames.

The selecting the common subset of the set of illustrative asset videoframes to produce the first portion of first descriptive asset videoframes of the first descriptive asset and to produce the first portionof second descriptive asset video frames of the second descriptive assetincludes a series of sub-steps. A first sub-step includes the instanceexperience module 290 determining required first descriptive asset videoframes of the first descriptive asset. At least some of the requiredfirst descriptive asset video frames includes at least some of the setof illustrative asset video frames. For example, the instance experiencemodule 290 determines the required first descriptive asset video frames402 based on the first set of knowledge bullet-points for the firstpiece of information regarding the topic. For instance, depictions ofthe engine associated with the detected engine error code.

A second sub-step includes determining required second descriptive assetvideo frames 404 of the second descriptive asset. At least some of therequired second descriptive asset video frames includes at least some ofthe set of illustrative asset video frames. For example, the instanceexperience module 290 determines the required second descriptive assetvideo frames 404 based on the second set of knowledge bullet-points forthe second piece of information regarding the topic. For instance,depictions of the engine associated with the detected engine error code.

A third sub-step includes identifying common video frames of therequired first descriptive asset video frames and the required seconddescriptive asset video frames as the common subset of the set ofillustrative asset video frames. For example, the instance experiencemodule 290 searches through the first and second descriptive asset videoframes to identify the common video frames that substantially match eachother as the common subset of the set of illustrative asset video frames400. These identified common video frames will not have to bere-rendered thus providing an improvement.

FIG. 10C further illustrates the example of the method of operation toselect the lesson package, where having selected the common subset ofthe set of illustrative asset video frames to produce the first portionsof the first and second descriptive asset video frames, a sixth step ofthe example method of operation of the selecting the lesson packageincludes the experience execution module 32 rendering a representationof the first set of knowledge bullet-points to produce a remainingportion of the first descriptive asset video frames of the firstdescriptive asset. The first descriptive asset video frames 402 includesthe common subset of the set of illustrative asset video frames 400.

The rendering the representation of the first set of knowledgebullet-points to produce the remaining portion of the first descriptiveasset video frames of the first descriptive asset includes a series ofsub-steps. A first sub-step includes the instance experience module 290determining required first descriptive asset video frames of the firstdescriptive asset (e.g., in totality based on the first set of knowledgebullet-points).

A second sub-step includes the instance experience module 290identifying the common subset of the set of illustrative asset videoframes within the required first descriptive asset video frames. Forexample, the instance experience module 290 identifies the common engineillustrative asset video frames associated with the required firstdescriptive asset video frames.

A third sub-step includes the instance experience module 290 identifyingremaining video frames of the required first descriptive asset videoframes as the remaining portion of the first descriptive asset videoframes. For example, the instance experience module 290 identifies othervideo frames of the first descriptive asset video frames.

A fourth sub-step includes the instance experience module 290 renderingthe identified remaining video frames of the required first descriptiveasset video frames to produce the remaining portion of the firstdescriptive asset video frames. For instance, the instance experiencemodule 290 renders video frames associated with unique aspects of therepresentation of the engine associated with the detected impairment(e.g., not including a need to re-render the common subset of the set ofillustrative asset video frames).

Having produced the first descriptive asset video frames 402, the sixthstep of the example method of operation to select the lesson packagefurther includes the instance experience module 290 rendering arepresentation of the second set of knowledge bullet-points to produce aremaining portion of the second descriptive asset video frames 404 ofthe second descriptive asset. The second descriptive asset video frames404 includes the common subset of the set of illustrative asset videoframes. For instance, the instance experience module 290 renders furthervideo frames associated with further unique aspects of therepresentation of the engine associated with the detected impairment(e.g., not including a need to re-render the common subset of the set ofillustrative asset video frames).

Having produced the first and second descriptive asset video frames 402and 404, a seventh step of the example method of operation of theselecting of the lesson package includes the experience execution module32 linking the first descriptive asset video frames of the firstdescriptive asset with the second descriptive asset video frames of thesecond descriptive asset to form at least a portion of themulti-disciplined learning tool. For example, the instance experiencemodule 290 integrates all the video frames of the first descriptiveasset video frames 402 as a representation of the first descriptiveasset and integrates all of the video frames of the second descriptiveasset video frames 404 is a representation of the second descriptiveasset.

Having linked the first descriptive asset video frames and the seconddescriptive asset video frames, an eighth step of the example method ofoperation of the selecting of the lesson package includes the experienceexecution module 32 outputting the multidisciplined learning tool (e.g.,now comprehensive training on engine repair) to include therepresentations of the first and second descriptive assets. For example,the instance experience module 290 outputs the representation of thefirst descriptive asset to a second computing entity (e.g., associatedwith the learner 28-1. The representation of the first descriptive assetincludes the remaining portion of the first descriptive asset videoframes and the common subset of the set of illustrative asset videoframes.

Having output the representation of the first descriptive asset, theexample further includes the instance experience module outputting therepresentation of the second descriptive asset to the second computingentity. The representation of the second descriptive asset includes theremaining portion of the second descriptive asset video frames and thecommon subset of the set of illustrative asset video frames.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 11A, 11B, 11C, and 11D are schematic block diagrams of anembodiment of a computing system illustrating an example of utilizing alesson package. The computing system includes the environment sensormodule 14 of FIG. 1, the experience execution module 32 of FIG. 1, andthe learning assets database 34 of FIG. 1. In an embodiment, theenvironment sensor module 14 includes the motion sensor 126 of FIG. 4and the position sensor 128 of FIG. 4. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330, all ofFIG. 9A.

FIG. 11A illustrates an example of a method of operation to utilize thelesson package where, in a first step of the example method theexperience execution module 32 creates a first-pass of first and secondlearning objects for an assembly topic. The creating includes theinstance experience module 290 creating a first-pass of the firstlearning object for a first piece of assembly information regarding theassembly topic to include a first set of knowledge bullet-pointsregarding the first piece of assembly information. As a specific exampleof assembly information, the first piece of assembly informationincludes components of a 4 cycle engine, depictions of the components inan operational example of the 4 cycle engine, and a disassembly order ofthe components when the assembly topic includes “how a 4 cycle engine isbuilt and works.” The creating of the first learning object includesderiving from a lesson package 206 of the learning assets database 34and generating based on an interpretation of the first set of knowledgebullet-points. When deriving from the lesson package 206, the instanceexperience module 290 interprets one or more of instruction information204 and baseline environment and object information 292 generated basedon the lesson package 206.

The creating of the learning objects further includes the instanceexperience module 290 creating a first-pass of the second learningobject for a second piece of assembly information regarding the assemblytopic to include a second set of knowledge bullet-points regarding thesecond piece of assembly information. The second set of knowledgebullet-points regarding the second piece of assembly information isdifferent than the first set of knowledge bullet-points regarding thefirst piece of assembly information. As another specific example ofassembly information, the second piece of assembly information includescomponents of the 4 cycle engine, depictions of the components in anoperational example of the 4 cycle engine, and an assembly order of thecomponents when the assembly topic includes “how a 4 cycle engine isbuilt and works.” The creating of the second learning object includesextracting from the lesson package 206 of the learning assets database34 and generating based on an interpretation of the second set ofknowledge bullet-points.

Having created the first-pass of first and second learning objects forthe assembly topics, in a second step of the example method theexperience execution module 32 obtains an illustrative asset based onthe first and second set of knowledge bullet-points. The illustrativeasset depicts an aspect regarding the assembly topic pertaining to thefirst and second pieces of assembly information (e.g., common componentsof the engine utilize to depict disassembly and assembly of the engine).The obtaining of the illustrative asset includes a variety ofapproaches. A first approach includes the instance experience module 290interpreting instructor input information to identify the illustrativeasset. For instance, the instructor highlights a cylinder wall ininstructions of both of disassembly and assembly procedures.

A second approach includes the instance experience module 290identifying a first object of the first and second set of knowledgebullet-points as the illustrative asset. For instance, a spark plug isidentified as a common object in both of the disassembly and assemblyprocedures.

A third approach includes the instance experience module 290 determiningthe illustrative asset based on the first object of the first and secondset of knowledge bullet-points. For instance, the spark plug is includedin the illustrative asset to facilitate subsequent rendering once toprovide a rendering efficiency for the computing system.

Having obtained the illustrative asset, in a third step of the examplemethod the experience execution module 32 renders a portion of theillustrative asset to produce a set of illustrative asset video frames.For example, the instance experience module 290 applies a videorendering approach to at least a portion of the illustrative asset togenerate the set of illustrative asset video frames. For instance, videoframes are generated for each of the cylinder wall, the spark plug, avalve, etc.

Having produced the set of illustrative asset video frames, in a fourthstep of the example method the experience execution module 32 creates asecond-pass of the first and second learning objects. The creating ofthe second-pass of the learning objects includes the instance experiencemodule 290 creating a second-pass of the first learning object tofurther include a first descriptive assembly asset (e.g., enginedisassembly) regarding the first piece of assembly information based onthe first set of knowledge bullet-points and the illustrative asset. Thefirst descriptive assembly asset includes first descriptive assemblyasset video frames (e.g., related to engine disassembly).

The creating of the second-pass of the first learning object includes aseries of sub-steps. A first sub-step includes the instance experiencemodule 290 selecting a subset of the set of illustrative asset videoframes to produce a portion of the first descriptive assembly assetvideo frames. For example, the instance experience module 290 selectsillustrative asset video frames that are common to the first and secondset of knowledge bullet-points to produce the subset of the set ofillustrative asset video frames.

A second sub-step includes the instance experience module 290 renderinga representation of the first set of knowledge bullet-points to producea remaining portion of the first descriptive assembly asset videoframes. For example, the instance experience module 290 renders uniqueaspects of the first set of knowledge bullet-points that are nototherwise included in the subset of the set of illustrative asset videoframes to fully represent the first set of knowledge bullet-points.

The creating of the second-pass of the learning objects further includesthe instance experience module 290 creating a second-pass of the secondlearning object to further include a second descriptive assembly (e.g.,engine assembly) asset regarding the second piece of assemblyinformation based on the second set of knowledge bullet-points and theillustrative asset. The second descriptive assembly asset includessecond descriptive assembly asset video frames (e.g., related to engineassembly).

The creating of the second-pass of the second learning object includes aseries of sub-steps. A first sub-step includes the instance experiencemodule 290 selecting the subset of the set of illustrative asset videoframes (e.g., same as those for the first descriptive assembly assetvideo frames) to produce a portion of the second descriptive assemblyasset video frames. For example, the instance experience module 290selects the same illustrative asset video frames that are common to thefirst and second set of knowledge bullet-points.

A second sub-step includes the instance experience module 290 renderinga representation of the second set of knowledge bullet-points to producea remaining portion of the second descriptive assembly asset videoframes. For example, the instance experience module 290 renders uniqueaspects of the second set of knowledge bullet-points that do nototherwise included in the subset of the set of illustrative asset videoframes to fully represent the second set of knowledge bullet-points.

Having produced the second-pass of the first and second learningobjects, in a fifth step of the example method the experience executionmodule 32 outputs the first descriptive assembly asset video frames to asecond computing entity for interactive consumption by a learning entity(e.g., the learner 28-1). For example, the instance experience module290 generates learner output information 172 as previously discussedbased on the first descriptive assembly asset video frames.

The instance experience module 290 sends the learner output information172 to a computing device associated with the learner 28-1 (e.g.,portraying the engine disassembly sequence). While outputting thelearner output information 172 (e.g., the video frames of the sequenceshowing virtual disassembly of the engine by the learner 28-1 asdepicted in FIG. 11B), the instance experience module 290 receiveslearner input information 174 from the second computing entity inresponse to the interactive consumption by the learning entity. Forinstance, the instance experience module 290 captures the learner inputinformation 174 from the learner 28-1 to produce learner interactioninformation 332 as previously discussed.

Having output the first descriptive asset video frames, the fifth stepof the example method further includes the instance experience module290 outputting the second descriptive assembly asset video frames to thesecond computing entity for further interactive consumption by thelearning entity. For example, the instance experience module 290 sendsfurther learner output information 172 to the computing deviceassociated with the learner 28-1 (e.g., portraying the engine assemblysequence). While outputting the further learner output information 172(e.g., the video frames of the sequence showing virtual reassembly ofthe engine by the learner 28-1 as depicted in FIG. 11C), the instanceexperience module 290 receives learner input information 174 from thesecond computing entity in response to the further interactiveconsumption by the learning entity. For instance, the instanceexperience module 290 captures the learner input information 174 fromthe learner 28-1 to produce further learner interaction information 332as previously discussed.

Having captured the learner input information 174, the fifth step of theexample method further includes the learning assessment module 330receiving environment sensor information 150 in response to theinteractive consumption by the learning entity. For example, theenvironment sensor module 14 senses motions of the learner 28-1 withregards to the disassembly and/or assembly of the engine and outputs theenvironment sensor information 150 to the learning assessment module 330and/or the instance experience module 290.

Having received the environment sensor information 150, the fifth stepof the example method further includes the instance experience module290 modifying the second-pass of the first learning object based on oneor more of the learner input information 174 and the environment sensorinformation 150 to portray the interactive consumption by the learningentity. For instance, the instance experience module 290 renders furthervideo frames of another sequence showing virtual reassembly of thedisassemble the engine by the learner 28-1 as further depicted in FIG.11C.

When the learning entity experiences the further interactiveconsumption, the instance experience module 290 receives further learnerinput information from the second computing entity in response tofurther interactive consumption by the learning entity and receivesfurther environment sensor information in response to the furtherinteractive consumption by the learning entity. Having received thefurther learner input information and the further environment sensorinformation, the instance experience module 290 modifies the second-passof the second learning object based on one or more of the furtherlearner input information and the further environment sensor informationto portray the further interactive consumption by the learning entity(e.g., as depicted by FIGS. 11B and 11C).

FIG. 11D further illustrates the example of the method of operation toutilize the lesson package where, in a sixth step the experienceexecution module 32 determines learning assessment results associatedwith the interactive consumption by the learning entity. The determiningthe learning assessment results associated with the interactiveconsumption by the learning entity includes a variety of approaches. Afirst approach includes the learning assessment module 330 obtaining arepresentation of the interactive consumption. For example, the learningassessment module 330 receives learner interaction information 332 fromthe instance experience module 290. As another example, the learningassessment module 330 interprets the environment sensor information 150from the environment sensor module 14.

A second approach includes the learning assessment module 330 evaluatingthe representation of the interactive consumption utilizing evaluationcriteria of assessment information 252 to produce the learningassessment results. For example, the learning assessment module 330compares aspects of the learner interaction information 332 and theenvironment sensor information 150 to the evaluation criteria toidentify undesired variations and desired performance.

A third approach includes the learning assessment module 330 identifyingan undesired performance aspect of the learning assessment results basedon the evaluation criteria of the assessment information. The undesiredperformance aspect includes one or more of an assembly error and adisassembly error. For example, the learning assessment module 330interprets the comparison of the aspects of the learner interactioninformation 332 and the environment sensor information 150 to theevaluation criteria and identifies an undesired performance aspect thatincludes an error in disassembly of the engine. As another example, thelearning assessment module 330 further interprets the comparison of theaspects of the learner interaction information 332 and the environmentsensor information 150 to the evaluation criteria and identifies anotherundesired performance aspect that includes an error in assembly of theengine.

Having generated the learning assessment results, the learningassessment module 330 facilitates storing of the learning assessmentresults. For example, the learning assessment module 330 stores thelearning assessment results as learning assessment results information334 in the learning assets database 34 to facilitate subsequent furtherenhanced learning.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 12A, 12B, and 12C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lessonpackage. The computing system includes the experience execution module32 of FIG. 1, the learning assets database 34 of FIG. 1, and theenvironment sensor module 14 of FIG. 1. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330, all ofFIG. 9A. In an embodiment, the environment sensor module 14 includes themotion sensor 126 of FIG. 4 and the position sensor 128 of FIG. 4.

FIG. 12A illustrates an example of operation of a method to modify alesson package where in a first step the experience execution module 32generates a representation of a portion of a lesson package 206, where aplurality of learning objects are associated with a plurality ofaugmenting multimedia content. For example, the environment generationmodule 240 generates learner output information 172 as previouslydiscussed based on instruction information 204, baseline environment andobject information 292 and assessment information 252. The environmentgeneration module 240 receives lesson package 206 from the learningassets database 34 and generates the assessment information 252, theinstruction information 204, and the baseline environment and objectinformation 292 based on the lesson package 206 as previously discussed.

The augmenting multimedia content includes one or more of a video clip,an audio clip, a textual string, etc. The augmenting multimedia contentis associated with one or more of the plurality of learning objectswhere the augmenting multimedia content embellishes the learning aspectsof the plurality of learning objects by providing further content in oneor more formats.

Having generated the representation, in a second step of the method tomodify the lesson package, the experience execution module 32, whileoutputting the representation to the learner 28-1, captures learnerinput information 174 to produce learner interaction information 332 aspreviously discussed. For instance, the learner output information 172illustrates an operational engine and the learner input information 174includes interactions of the learner 28-1 with the representation of theoperational engine.

Having produced the learner interaction information 332, in a third stepof the method to modify the lesson package, the experience executionmodule 32, while outputting the learner output information 172 to thelearner 28-1, captures environment sensor information 150 representinglearner manipulation of the representation as previously discussed. Forinstance, the environment sensor information 150 captures the learner28-1 identifying an area of interest of the operational engine.

FIG. 12B further illustrates the example of operation of the method tomodify the lesson package, where having produced the learner interactioninformation 332 and captured the environment sensor information 150, ina fourth step the experience execution module 32 analyzes the learnerinteraction information 332 and the environment sensor information 150based on the assessment information 252 to produce learning assessmentresults information 334 as previously discussed. For example, thelearning assessment module 330 generates the learning assessment resultsinformation 334 to identify an area for improved learning associatedwith the representation.

Having produced the learning assessment results information 334, theexperience execution module 32 selects and augmenting multimedia contentbased on the learning assessment results information 334. For example,the environment generation module 240 identifies the augmentingmultimedia content associated with the area for improved learning.Having selected the augmenting multimedia content, in a sixth step theexperience execution module 32 generates an updated representation ofthe portion of the lesson package to include the selected augmentingmultimedia content. For example, the environment generation module 240modifies the instruction information 204 and/or the baseline environmentand object information 292 to include the selected augmenting multimediacontent.

The instance experience module 290 regenerates the learner outputinformation 172 utilizing the modified instruction information 204and/or the modified baseline environment and object information 292 toinclude the selected augmenting multimedia content. For instance, asillustrated in FIG. 12C, the instance experience module 290 inserts asingle explosion multimedia clip into the learner output renderingsequence 2 of an enhanced power stroke rendering to further enhance theexperience of the learner 28-1 in understanding the operational engine.

Having generated the updated representation, in a seventh step of themethod to modify the lesson package, the experience execution moduleoutputs the updated representation to the learner 28-1 to enhancelearning. For example, the instance experience module 290 outputs themodified learner output information 172 to the learner 28-1 where theenhanced power stroke rendering now includes the single explosionmultimedia clip.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 13A, 13B, and 13C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lessonpackage. The computing system includes the experience execution module32 of FIG. 1, the environment sensor module 14 of FIG. 1, and thelearning assets database 34 of FIG. 1. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330, all ofFIG. 9A.

FIG. 13A illustrates an example of a method of operation to modify thelesson package, where, in a first step the experience execution module32 generates a representation of a portion of a lesson package 206 for aset of learners 28-1 through 28-N. For example, the environmentgeneration module 240 generates learner output information 172 aspreviously discussed based on instruction information 204, baselineenvironment and object information 292 and assessment information 252.The environment generation module 240 receives lesson package 206 fromthe learning assets database 34 and generates the assessment information252, the instruction information 204, and the baseline environment andobject information 292 based on the lesson package 206 as previouslydiscussed.

Having generated the representation, in a second step of the method tomodify the lesson package, while outputting the representation to theset of learners, the experience execution module 32 captures learnerinput information 174 to produce learner interaction information 332 aspreviously discussed but for the set of learners. Having produced thelearner interaction information 332, the experience execution module 32,while outputting the representation, in a third step of the method tomodify the lesson package, the experience execution module 32 capturesenvironment sensor information 150 representing interaction of the setof learners with the representation.

FIG. 13B further illustrates the example of the method of operation tomodify the lesson package, where, in a fourth step the experienceexecution module 32 analyzes the learner interaction information 332 andthe environment sensor information 150 based on the assessmentinformation 252 to produce learning assessment results information 334as previously discussed. For example, the learning assessment module 330produces the learning assessment results information 334 to indicatewhich parts of the portion of the lesson package that the set oflearners are most affiliated with (e.g., interested in, spending timeviewing, etc.).

Having produced the learning assessment results information 334, in afifth step the experience execution module 32 selects insert brandingcontent based on the learning assessment results information 334. Theinsert branding content includes one or more of a video clip, an image,text, etc. associated with a brand. The selecting is based on one ormore of finding a brand that sells with the set of learners,demographics of the learners, past sell through history, and anassessment of understanding. For example, the environment generationmodule 240 selects a spark plug brand over a valve brand when the set oflearners are more affiliated with replacing spark plugs than replacingvalves of an engine and the representation is associated with theengine.

Having selected the insert branding content, in a 6 step of the methodof operation to modify the lesson package, the experience executionmodule 32 generates an updated representation of the portion of thelesson package to include the selected insert branding content. Forexample, the environment generation module 240 provides updatedinstruction information 204 and/or baseline environment and objectinformation 292 based on the selected insert branding extracted fromlesson package 206 of the learning assets database 34.

The instance experience module 290 generates modified learner outputinformation 172, as illustrated in FIG. 13C, utilizing the modifiedinstruction information 204 and/or modified baseline environment andobject information 292 that includes the selected insert brandingcontent. For example, the instance experience module 290 produces themodified learner output information 172 to include an image of a sparkplug and text that reads “legendary brand spark plugs from cool” next tothe engine rendering for the enhanced power stroke of learner outputrendering sequence 2.

Having produced the modified learner output information 172, in aseventh step of the method of operation to modify the lesson package,the experience execution module 32 outputs the updated representation ofthe portion of the lesson package to the set of learners 28-1 through28-N. For example, the instance experience module 290 outputs themodified learner output information 172 that includes the spark plugbrand content to the set of learners.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 14A and 14B are schematic block diagrams of an embodiment of acomputing system illustrating an example of modifying a lesson package.The computing system includes the experience execution module 32 of FIG.1, the environment sensor module 14 of FIG. 1, and the learning assetsdatabase 34 of FIG. 1. The experience execution module 32 includes theenvironment generation module 240, the instance experience module 290,and the learning assessment module 330, all of FIG. 9A.

FIG. 14A illustrates an example of a method of operation to modify thelesson package, where, in a first step the experience execution module32 generates a set of representations of a portion of a lesson package206 for a set of learners 28-1 through 28-N, where each representationis substantially unique for an associated learner (e.g., uniqueviewpoint). For example, the environment generation module 240 generateslearner output information 172-1 through 172-N as previously discussedbased on instruction information 204, baseline environment and objectinformation 292 and assessment information 252. The environmentgeneration module 240 receives lesson package 206 from the learningassets database 34 and generates the assessment information 252, theinstruction information 204, and the baseline environment and objectinformation 292 based on the lesson package 206 as previously discussed.

Having generated the set of representations, in a second step of themethod to modify the lesson package, while outputting the set ofrepresentations to the set of learners, the experience execution module32 captures learner input information 174-1 through 174-N to producelearner interaction information 332 as previously discussed but for theset of learners. Having produced the learner interaction information332, the experience execution module 32, while outputting the set ofrepresentations, in a third step of the method to modify the lessonpackage, the experience execution module 32 captures environment sensorinformation 150 representing interaction of the set of learners with theset of representations.

FIG. 14B further illustrates the example of the method of operation tomodify the lesson package, where, in a fourth step the experienceexecution module 32 analyzes the learner interaction information 332 andthe environment sensor information 150 based on the assessmentinformation 252 to produce learning assessment results information 334as previously discussed, but for the set of learners. For example, thelearning assessment module 330 produces the learning assessment resultsinformation 334 to indicate which parts of the portion of the lessonpackage that the set of learners struggle with and which parts theylearn effectively.

Having produced the learning assessment results information 334, in afifth step the experience execution module 32 identifies one or morerepresentations of the set of representations that optimizes learning.For example, the learning assessment module 330 identifies a portion ofthe lesson package that the set of learners learn effectively from. In asixth step, the experience execution module 32 updates the lessonpackage to include the identified one or more representations of the setof representations that optimizes learning. For example, the learningassessment module 330 facilitates updating of the lesson package 206 toproduce an updated lesson package that includes the identified one ormore representations of the set of representations that optimizeslearning. Having produced the updated lesson package, the learningassessment module 330 stores the updated lesson package in the learningassets database 34 to facilitate utilization by even further learners toutilize the identified one or more representations to experienceenhanced learning.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 15A, 15B, and 15C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lessonpackage. The computing system includes the experience execution module32 of FIG. 1, the environment sensor module 14 of FIG. 1, and thelearning assets database 34 of FIG. 1. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330, all ofFIG. 9A.

FIG. 15A illustrates an example of a method of operation to modify thelesson package, where, in a first step the experience execution module32 generates a representation of a portion of a lesson package 206 thatincludes a set of objects. For example, the environment generationmodule 240 generates learner output information 172 as previouslydiscussed based on instruction information 204, baseline environment andobject information 292 and assessment information 252. The environmentgeneration module 240 receives lesson package 206 from the learningassets database 34 and generates the assessment information 252, theinstruction information 204, and the baseline environment and objectinformation 292 based on the lesson package 206 as previously discussed.

Having generated the representation, in a second step of the method tomodify the lesson package, while outputting the representation to thelearner 28-1, the experience execution module 32 captures learner inputinformation 174 to produce learner interaction information 332 aspreviously discussed. Having produced the learner interactioninformation 332, the experience execution module 32, while outputtingthe representation, in a third step of the method to modify the lessonpackage, the experience execution module 32 captures environment sensorinformation 150 representing learner manipulation of the representation.

FIG. 15B further illustrates the example of the method of operation tomodify the lesson package, where, in a fourth step the experienceexecution module 32 analyzes the learner interaction information 332 andthe environment sensor information 150 based on the assessmentinformation 252 to produce learning assessment results information 334as previously discussed, but to identify performance as a function of arepresentation attribute. The attribute includes one or more of size,scale relationship with another object representation, color, shading,flashing, playback speed, etc. For example, the learning assessmentmodule 330 produces the learning assessment results information 334 toindicate which object of the set objects should be highlighted toenhance learning.

Having produced the learning assessment results information 334, in afifth step the experience execution module 32 updates the representationof the portion of the lesson package based on the learning assessmentresults information 334, where the updated portion is generatedutilizing an updated representation attribute. For example, the instanceexperience module 290 determines the updated representation attribute toinclude enlarging the bucket of a representation of a bulldozer when thelearning assessment results information 334 indicates that enlarging thesize of the bucket object relative to the rest of the bulldozer enhancesthe learning associated with the bucket object. Having determined theupdated representation attribute, the instance experience module 290updates the learner output information 172 utilizing the updatedrepresentation attribute as illustrated in FIG. 15C where in a learneroutput rendering sequence 2 the scale of the scoop of the bulldozerobject is enlarged and the scale of the bulldozer object is reduced.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 16A, 16B, and 16C are schematic block diagrams of an embodiment ofa computing system illustrating an example of modifying a lessonpackage. The computing system includes the experience execution module32 of FIG. 1, the environment sensor module 14 of FIG. 1, and thelearning assets database 34 of FIG. 1. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330, all ofFIG. 9A.

FIG. 16A illustrates an example of a method of operation to modify thelesson package, where, in a first step the experience execution module32 generates a first representation of a portion of a lesson package 206for a first learner book a set of learners 28-1 through 28-N, where eachrepresentation is substantially unique for an associated learner (e.g.,unique viewpoint). For example, the environment generation module 240generates learner output information 172-1 through 172-N as previouslydiscussed based on instruction information 204, baseline environment andobject information 292 and assessment information 252. The environmentgeneration module 240 receives lesson package 206 from the learningassets database 34 and generates the assessment information 252, theinstruction information 204, and the baseline environment and objectinformation 292 based on the lesson package 206 as previously discussed.

Having generated the first representation, in a second step of themethod to modify the lesson package, while outputting the firstrepresentation to the first learner, the experience execution module 32captures first learner input information 174-1 to produce first learnerinteraction information 332-1 of learner interaction information 332-1through 332-N as previously discussed but for the set of learners.Having produced the first learner interaction information 332-1 theexperience execution module 32, while outputting the first learnerrepresentation to the first learner, in a third step of the method tomodify the lesson package, the experience execution module 32 capturesfirst environment sensor information 150-1 representing first learnermanipulation of the first representation.

FIG. 16B further illustrates the example of the method of operation tomodify the lesson package, where, in a fourth step the experienceexecution module 32 analyzes the first learner interaction information332-1 and the first environment sensor information 150-1 based on theassessment information 252 to produce first learning assessment resultsinformation 334-1 that identifies performance as a function of arepresentation attribute. For example, the learning assessment module330 produces the learning assessment results information 334 to indicatewhich parts of the portion of the lesson package that the first learnerstruggles with and which parts the first learner learns effectively.

Having produced the first learning assessment results information 334-1,in a fifth step the experience execution module 32 generates a secondrepresentation of the portion of the lesson package for a second learnerof the set of learners based on the first learning assessment results,where the second representation is further generated utilizing anupdated representation attribute. For example, the instance experiencemodule 290 determines the updated representation attribute to be aslower playback speed to enhance learning of the portion of the lessonpackage for the second learner.

The instance experience module 290 generates learner output information172-2 for the second learner utilizing the updated representationattribute. For example, as illustrated in FIG. 16 C, the instanceexperience module 290 generates the learner output information 172-2 toinclude second learner output rendering sequences 1 and 2 for just anintake stroke engine illustration when the first representation producedlearner output information 172-1 where just a first learner outputrendering sequence 1 was associated with the intake stroke.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 17A, 17B, and 17C are schematic block diagrams of an embodiment ofa computing system illustrating an example of selecting and utilizing alesson package in a virtual world environment. The computing systemincludes the experience execution module 32 of FIG. 1, the environmentsensor module 14 of FIG. 1, and the learning assets database 34 ofFIG. 1. The experience execution module 32 includes the environmentgeneration module 240, the instance experience module 290, and thelearning assessment module 330, all of FIG. 9A.

FIG. 17A illustrates an example of a method of operation to select andutilize the lesson package in the virtual world environment, where, in afirst step the experience execution module 32 selects a lesson packagefrom a plurality of lesson packages based on a learner requirement for alearner 28-X to produce a selected lesson package. The learnerrequirement includes at least one of a topic requirement of a topic(e.g., a desired topic and topic complexity level for the specificlearner) and a virtual world requirement (e.g., a favorite, one thattypically portrays the lesson package, etc.).

The selected lesson package includes a first learning object and asecond learning object. The first learning object includes a first setof knowledge bullet-points regarding a first piece of informationregarding the topic. The second learning object includes a second set ofknowledge bullet-points regarding a second piece of informationregarding the topic.

The first and second learning objects include an illustrative assetbased on the first and second set of knowledge bullet-points. Theillustrative asset depicts an aspect regarding the topic pertaining tothe first and second pieces of information. The first learning objectfurther includes a first descriptive asset regarding the first piece ofinformation based on the first set of knowledge bullet-points and theillustrative asset. The second learning object further includes a seconddescriptive asset including the second piece of information based on thesecond set of knowledge bullet-points and the illustrative asset.

As an example of the selecting the lesson package, the environmentgeneration module 240 interprets the learner requirement from learnerinput information 174-X from the learner 28-X and selects a lessonpackage 206-X from the lesson packages 206-1 through 206-N from thelearning assets database 34. As another example of the selecting thelesson package, the instance experience module 290 further interpretsthe learner input information 174-X to determine the virtual worldrequirement for the learner 28-X and selects the lesson package 206-Xwhen the lesson package 206-X is favorably compatible with the virtualworld requirement.

Having selected the lesson package, a second step of the example methodof operation includes the experience execution module 32 generating afirst instance of execution of the selected lesson package in a firstactive virtual world environment and generating a second instance ofexecution of the selected lesson package in a second active virtualworld environment. For example, the instance experience module 290generates a plurality of representations of the selected lesson package,and in an embodiment a plurality of other lesson packages, for aplurality of learners 28-1 through 28-N.

The plurality of lesson packages are associated with a massive number ofactive virtual world environments. Each active virtual world environmentincludes a plurality of objects that interact with each other and a setof associated learners that interact with the plurality of objects inaccordance with inputs from the set of associated learners and learningobjects of associated lesson packages. The active virtual world includesseveral objectives such as providing training and education. The activevirtual world further includes an objective of entertainment. The activevirtual world further includes a combination of education andentertainment (e.g., edutainment).

As an example of the generating of the plurality of representations, theenvironment generation module 240 generates learner output information172-1 through 172-N as previously discussed based on instructioninformation 204-1 through 204-N, baseline environment and objectinformation 292-1 through 292-N, and assessment information 252-1through 252-N. The environment generation module 240 receives lessonpackages 206-1 through 206-N associated with the massive number ofactive virtual world environments from the learning assets database 34and generates the assessment information 252-1 through 252-N, theinstruction information 204-1 through 204-N, and the baselineenvironment and object information 292-1 through 292-N based on thelesson packages 206-1 through 206-N as previously discussed on anindividual basis.

Having generated the instances of the selected lesson package, theexample method of operation includes a third step where the experienceexecution module evaluates for at least some of the set of activevirtual world environments, at least one of learner interactioninformation and environmental sensor information associated with theexecution of an associated instance of execution of the selected lessonpackage to produce learning assessment results for the selected lessonpackage.

As an example of producing the learning assessment results for theselected lesson package, the learning assessment module 330 generateslearning assessment results information 334-1 through 334-N thatindicates learning effectiveness, based on comparing the assessmentinformation 252-1 through 252-N to environment sensor information 150-1through 150-N and learner interaction information 332-1 through 332-N.The environment sensor information is produced by the environment sensormodule 14 in response to monitoring of the learners 28-1 through 28-N asthe experience the selected lesson package in the plurality of activevirtual world environments. The instance experience module 290interprets learner input information 174-1 through 134-N from thelearners 28-1 through 28-N to produce the learner interactioninformation 332-1 through 332-N as the learners experience learneroutput information 172-1 through 172-N representing the plurality ofactive virtual world environments.

FIG. 17B further illustrates the example of the method of operation toselect and utilize the lesson package in the virtual world environment,where, having produced the plurality of learning assessment results, ina fourth step the experience execution module 32 identifies a set ofactive virtual world environments that each include a different instanceof execution of the selected lesson package. A first instance ofexecution of the selected lesson package includes first descriptiveasset video frames of the first descriptive asset and second descriptiveasset video frames of the second descriptive asset within a first activevirtual world environment of the set of active virtual worldenvironments. The first descriptive asset video frames and the seconddescriptive asset video frames include a common subset of illustrativeasset video frames so that subsequent utilization of the common subsetof illustrative asset video frames reduces rendering of other first andsecond descriptive asset video frames. The common subset of illustrativeasset video frames are selected from a set of illustrative asset videoframes rendered from the illustrative asset.

As an example of identifying the set of active virtual worldenvironments, the instance experience module 290 identifies a set ofinstances of learner output information that includes the selectedlesson package. For instance, the instance experience module 290determines that the selected lesson package is included in the learneroutput 172-1 and 172-2 among others as illustrated in FIG. 17C.

Having identified the set of active virtual world environments thatinclude the selected lesson package, a fifth step of the example methodof operation includes the experience execution module 32 selecting oneactive virtual world environment of the set of active virtual worldenvironments based on at least one of the learner requirement andlearning assessment results for the selected lesson package to produce aselected virtual world environment. The selecting the one active virtualworld environment of the set of active virtual world environments basedon at least one of the learner requirement and learning assessmentresults for the selected lesson package to produce the selected virtualworld environment includes one or more approaches.

A first approach includes the learning assessment module 330 identifyinga first learning assessment result of a first active virtual worldenvironment that exceeds a minimum learning assessment resultexpectation threshold level. The learning assessment results include thefirst learning assessment result. The first active virtual worldenvironment includes the selected virtual world environment. Forinstance, the learning assessment module 330 selects the first activevirtual world environment when a score of the first learning assessmentresult exceeds the minimum learning assessment result expectationthreshold level for the first active virtual world environment. Theinstance experience module 290 identifies the first active virtual worldenvironment as the selected virtual world environment. For instance, theinstance experience module 290 identifies the active virtual worldenvironment associated with the learner output information 172-2 as theselected virtual world environment as illustrated in FIG. 17C.

A second approach includes the learning assessment module 330identifying a second learning assessment result of a second activevirtual world environment that exceeds the first learning assessmentresult of the first active virtual world environment. The learningassessment results include the second learning assessment result. Thesecond active virtual world environment includes the selected virtualworld environment. For instance, the learning assessment module 330selects the second active virtual world environment when a score of thesecond learning assessment result is greater than the score of the firstlearning assessment results. The instance experience module 290identifies the second active virtual world environment as the selectedvirtual world environment.

A third approach includes the instance experience module 290 comparingthe learner requirement to estimated experience expectations associatedwith at least some of the set of active virtual world environments toidentify the one active virtual world environment that is estimated todeliver more than a minimum threshold level of sub-requirements of thelearner requirement. For example, the instance experience module 290estimates that the one active virtual world environment should delivermore than a minimum threshold number of sub-requirements of the learnerrequirement. The instance experience module 290 identifies the oneactive virtual world environment as the selected virtual worldenvironment.

A fourth approach includes the instance experience module 290 comparingthe learner requirement to the estimated experience expectationsassociated with the at least some of the set of active virtual worldenvironments to identify the one active virtual world environment thatis estimated to deliver the highest number of the sub-requirements ofthe learner requirement. For example, the instance experience module 290estimates that the one active virtual world environment should deliverthe most number of sub-requirements of the learner requirement. Theinstance experience module 290 identifies the one active virtual worldenvironment as the selected virtual world environment.

Having produced the selected virtual world environment, a sixth step ofthe example method of operation includes the experience execution module32 rendering updated first descriptive asset video frames of the firstdescriptive asset and updated second descriptive asset video frames ofthe second descriptive asset within the selected virtual worldenvironment to produce a new video stream for the learner. The updatedfirst descriptive asset video frames and the updated second descriptiveasset video frames include the common subset of illustrative asset videoframes. The rendering the updated first descriptive asset video framesof the first descriptive asset and updated second descriptive assetvideo frames of the second descriptive asset within the selected virtualworld environment to produce the new video stream for the learnerincludes a series of sub-steps.

A first sub-step includes the instance experience module 290 selectingthe common subset of the set of illustrative asset video frames toproduce a first portion of the updated first descriptive asset videoframes of the first descriptive asset and to produce a first portion ofthe updated second descriptive asset video frames of the seconddescriptive asset, so that subsequent utilization of the common subsetof the set of illustrative asset video frames reduces rendering of otherupdated first and second descriptive asset video frames. For instance,the instance experience module 290 selects common video frames of theillustrative asset that are utilized by both the first learning objectand the second learning object.

A second sub-step includes the instance experience module 290 renderinga representation of the first set of knowledge bullet-points within theselected virtual world environment to produce a remaining portion of theupdated first descriptive asset video frames of the first descriptiveasset. The updated first descriptive asset video frames include thecommon subset of the set of illustrative asset video frames. Forinstance, the instance experience module 290 generates the unique videoframes for the first learning object that are outside of theillustrative asset video frames.

A third sub-step includes the instance experience module rendering arepresentation of the second set of knowledge bullet-points within theselected virtual world environment to produce a remaining portion of theupdated second descriptive asset video frames of the second descriptiveasset, wherein the updated second descriptive asset video framesincludes the common subset of the set of illustrative asset videoframes. For instance, the instance experience module 290 generates theunique video frames for the second learning object that are outside ofthe illustrative asset video frames.

A fourth sub-step includes the instance experience module 290 linkingthe updated first descriptive asset video frames of the firstdescriptive asset with the updated second descriptive asset video framesof the second descriptive asset to form at least a portion of the newvideo stream. For example, the instance experience module 290 links thevideo frames to produce learner output information 172-X for the learner28-X.

Having produced the new video stream, the example a method of operationfurther includes the experience execution module 32 outputting the newvideo stream to a second computing entity associated with the learner.For example, the instance experience module 290 outputs the learneroutput information 172-X to a computer associated with the learner 28-Xto provide visualization of the selected virtual world environment withthe selected lesson package. While outputting the learner outputinformation 172-X the instance experience module 290 receives learnerinput information 174-X from the learner 28-X to facilitate updating ofthe learner output information 172-X as the learner 28-X, among otherfactors, interacts with the selected virtual world environment.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, a seventh memory element etc.) that stores operationalinstructions can, when executed by one or more processing modules of theone or more computing devices of the computing system 10, cause the oneor more computing devices to perform any or all of the method stepsdescribed above.

FIGS. 18A, 18B, and 18C are schematic block diagrams of an embodiment ofa computing system illustrating an example of representing a lessonpackage. The computing system includes the learning assets database 34FIG. 1 and the experience execution module 32 of FIG. 1. The experienceexecution module 32 includes the environment generation module 240 andthe instance experience module 290, both of FIG. 9A.

FIG. 18A illustrates an example of a method of operation to representthe lesson package, where, in a first step the experience executionmodule 32 determines a set of lesson package requirements for a learner.The determining includes interpreting a received input from the learner28-1, accessing records for the learner 28-1 as part of lesson package206 from the learning assets database 34, identifying an educationaland/or training need of the learner 28-1 and identifying andentertainment needs of the learner 28-1. For example, the environmentgeneration module 240 interprets learner input information 174 from thelearner 28-1 to produce the set of lesson package requirements thatindicates bulldozer operation training is desired.

Having produced the set of lesson package requirements for the learner,in a second step of the method to represent the lesson package, theexperience execution module 32 selects a lesson package 206 for thelearner based on the set of lesson package requirements, where thelesson package 206 is associated with a baseline for dimensional model(e.g., 3 dimensions and time). For example, the environment generationmodule 240 accesses the learning assets database 34 to identify thelesson package 206 associated with bulldozer operation. The environmentgeneration module 240 generates the assessment information 252, theinstruction information 204, and the baseline environment and objectinformation 292 based on the lesson package 206 as previously discussed.The instance experience module 290 extracts rendering frames of aportion of the selected lesson package. For example, a first frameillustrates the bulldozer in a starting position, and subsequentsequential frames illustrate the bulldozer raising the scoop to a fullyraised position by frame 100.

FIG. 18B further illustrates the example of the method of operation torepresent the lesson package, where, having selected the lesson package206, in a third step the experience execution module 32 determines aperception requirement for the learner. The perception requirementindicates a ratio of perception of the fourth dimension of the baselinefour dimensional model of the lesson package to a fourth dimension of alearner four dimensional model. For example, the learner 28-1subsequently experiences and perceives the representation in a real-timefashion when a perception ratio of the two is 1:1. As another example,the learner 28-1 subsequently experiences and perceives therepresentation 10 times slower than the original real-time of thebaseline when the perception ratio is 10:1. As yet another example, thelearner 28-1 subsequently experiences and perceives the representation10 times faster than the original real-time of the baseline when theperception ratio is 1:10. For instance, 10 minutes of baseline seemslike one minute to the learner 28-1.

The determining of the perception requirement includes interpretinglearner input information 174 from the learner 28-1, identifying aprevious perception requirement associated with effective education,entertainment, and/or training. For instance, 100 frames of the baselinerepresentation seems like 10 frames to the learner 28-1 when theinstance experience module 290 determines the perception requirement forthe learner to include the 1:10 perception ratio based on interpretingthe learner input information 174.

Having determined the perception requirement, in a fourth step of themethod of operation to represent the lesson package, the experienceexecution module 32 determines a perception approach for representingthe selected lesson package to the learner based on the perceptionrequirement, where the perception approach maps the baseline fordimensional model to the learner for dimensional model. The perceptionapproach includes filling frames of a learner output information 172-Xwith replicated frames of the baseline when the learner establishes aperception requirement to be slower than the baseline (e.g., looks likeslow-motion).

The perception approach further includes interpreting a set of frames ofthe baseline to produce an output frame for the learner outputinformation 172-X when the learner establishes a perception requirementto be faster than the baseline (e.g., not to look like fast-forward butrather to represent a perception of multiple baseline frames with onelearner output frame). When interpreting the set of frames of thebaseline to produce one output frame for the learner output information172-X, the perception approach further includes smoothing the set ofbaseline frames, averaging the set of baseline frames, random pickingone of the set of baseline frames, selecting another one of the set ofbaseline frames that best represents the set of baseline frames,selecting a starting frame of the set of baseline frames, selecting amiddle frame of the set of baseline frames, and selecting an endingframe of the set of baseline frames.

FIG. 18C further illustrates the example of the method of operation torepresent the lesson package, where, having determined the perceptionapproach, in a fifth step the experience execution module 32 generates arepresentation of the selected lesson package utilizing the perceptionapproach, where the representation is in the learner for dimensionalmodel. The generating includes the instance experience module 290rendering frames for the learner output information 172-X from theframes of the baseline in accordance with the perception approach. Therendering includes rendering fewer frames than the original baselinewhen the time perception is to be faster than the original and renderingmore frames than the original baseline when the time perception is to beslower than the original. As another example, one year of baselineframes may be represented as one second of learner time when the onesecond of frames for the learner output information 172-X captures theperception of the one year of baseline frames.

Having generated the representation as learner output information 172-X,the instance experience module 290 outputs the learner outputinformation 172-X to the learner 28-1. The learner 28-1 perceives thelearner output information 172-X in accordance with the perceptionrequirement for the learner.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples.

What is claimed is:
 1. A computer-implemented method for utilizing alesson package in a virtual world environment, the method comprises:selecting, by a computing entity, a lesson package from a plurality oflesson packages based on a learner requirement for a learner to producea selected lesson package, wherein the learner requirement includes atleast one of a topic requirement of a topic and a virtual worldrequirement, wherein the selected lesson package includes a firstlearning object and a second learning object, wherein the first learningobject includes a first set of knowledge bullet-points regarding a firstpiece of information regarding the topic, wherein the second learningobject includes a second set of knowledge bullet-points regarding asecond piece of information regarding the topic, wherein the first andsecond learning objects include an illustrative asset based on the firstand second set of knowledge bullet-points, wherein the illustrativeasset depicts an aspect regarding the topic pertaining to the first andsecond pieces of information, wherein the first learning object furtherincludes a first descriptive asset regarding the first piece ofinformation based on the first set of knowledge bullet-points and theillustrative asset, wherein the second learning object further includesa second descriptive asset including the second piece of informationbased on the second set of knowledge bullet-points and the illustrativeasset; identifying, by the computing entity, a set of active virtualworld environments that each include a different instance of executionof the selected lesson package, wherein a first instance of execution ofthe selected lesson package includes first descriptive asset videoframes of the first descriptive asset and second descriptive asset videoframes of the second descriptive asset within a first active virtualworld environment of the set of active virtual world environments,wherein the first descriptive asset video frames and the seconddescriptive asset video frames include a common subset of illustrativeasset video frames so that subsequent utilization of the common subsetof illustrative asset video frames reduces rendering of other first andsecond descriptive asset video frames, wherein the common subset ofillustrative asset video frames are selected from a set of illustrativeasset video frames rendered from the illustrative asset; selecting, bythe computing entity, one active virtual world environment of the set ofactive virtual world environments based on at least one of the learnerrequirement and learning assessment results for the selected lessonpackage to produce a selected virtual world environment; and rendering,by the computing entity, updated first descriptive asset video frames ofthe first descriptive asset and updated second descriptive asset videoframes of the second descriptive asset within the selected virtual worldenvironment to produce a new video stream for the learner, wherein theupdated first descriptive asset video frames and the updated seconddescriptive asset video frames include the common subset of illustrativeasset video frames.
 2. The method of claim 1 further comprises:generating, by the computing entity, a first instance of execution ofthe selected lesson package in a first active virtual world environment;and generating, by the computing entity, a second instance of executionof the selected lesson package in a second active virtual worldenvironment.
 3. The method of claim 1 further comprises: evaluating, bythe computing entity, for at least some of the set of active virtualworld environments, at least one of learner interaction information andenvironmental sensor information associated with execution of anassociated instance of execution of the selected lesson package toproduce the learning assessment results for the selected lesson package.4. The method of claim 1 further comprises: outputting, by the computingentity, the new video stream to a second computing entity associatedwith the learner.
 5. The method of claim 1, wherein the selecting theone active virtual world environment of the set of active virtual worldenvironments based on at least one of the learner requirement and thelearning assessment results for the selected lesson package to producethe selected virtual world environment comprises one or more of:identifying a first learning assessment result of a first active virtualworld environment that exceeds a minimum learning assessment resultexpectation threshold level, wherein the learning assessment resultsinclude the first learning assessment result, wherein the first activevirtual world environment includes the selected virtual worldenvironment; identifying a second learning assessment result of a secondactive virtual world environment that exceeds the first learningassessment result of the first active virtual world environment, whereinthe learning assessment results include the second learning assessmentresult, wherein the second active virtual world environment includes theselected virtual world environment; comparing the learner requirement toestimated experience expectations associated with at least some of theset of active virtual world environments to identify the one activevirtual world environment that is estimated to deliver more than aminimum threshold level of sub-requirements of the learner requirement;and comparing the learner requirement to the estimated experienceexpectations associated with the at least some of the set of activevirtual world environments to identify the one active virtual worldenvironment that is estimated to deliver a highest number of thesub-requirements of the learner requirement.
 6. The method of claim 1,wherein the rendering the updated first descriptive asset video framesof the first descriptive asset and the updated second descriptive assetvideo frames of the second descriptive asset within the selected virtualworld environment to produce the new video stream for the learnercomprises: selecting the common subset of illustrative asset videoframes to produce a first portion of the updated first descriptive assetvideo frames of the first descriptive asset and to produce a firstportion of the updated second descriptive asset video frames of thesecond descriptive asset, so that subsequent utilization of the commonsubset of illustrative asset video frames reduces rendering of otherupdated first and second descriptive asset video frames; rendering arepresentation of the first set of knowledge bullet-points within theselected virtual world environment to produce a remaining portion of theupdated first descriptive asset video frames of the first descriptiveasset, wherein the updated first descriptive asset video frames includethe common subset of illustrative asset video frames; rendering arepresentation of the second set of knowledge bullet-points within theselected virtual world environment to produce a remaining portion of theupdated second descriptive asset video frames of the second descriptiveasset, wherein the updated second descriptive asset video framesincludes the common subset of illustrative asset video frames; andlinking the updated first descriptive asset video frames of the firstdescriptive asset with the updated second descriptive asset video framesof the second descriptive asset to form at least a portion of the newvideo stream.
 7. A computing device of a computing system, the computingdevice comprises: an interface; a local memory; and a processor operablycoupled to the interface and the local memory, wherein the local memorystores operational instructions that, when executed by the processor,causes the computing device to: select a lesson package from a pluralityof lesson packages based on a learner requirement for a learner toproduce a selected lesson package, wherein the learner requirementincludes at least one of a topic requirement of a topic and a virtualworld requirement, wherein the selected lesson package includes a firstlearning object and a second learning object, wherein the first learningobject includes a first set of knowledge bullet-points regarding a firstpiece of information regarding the topic, wherein the second learningobject includes a second set of knowledge bullet-points regarding asecond piece of information regarding the topic, wherein the first andsecond learning objects include an illustrative asset based on the firstand second set of knowledge bullet-points, wherein the illustrativeasset depicts an aspect regarding the topic pertaining to the first andsecond pieces of information, wherein the first learning object furtherincludes a first descriptive asset regarding the first piece ofinformation based on the first set of knowledge bullet-points and theillustrative asset, wherein the second learning object further includesa second descriptive asset including the second piece of informationbased on the second set of knowledge bullet-points and the illustrativeasset; identify a set of active virtual world environments that eachinclude a different instance of execution of the selected lessonpackage, wherein a first instance of execution of the selected lessonpackage includes first descriptive asset video frames of the firstdescriptive asset and second descriptive asset video frames of thesecond descriptive asset within a first active virtual world environmentof the set of active virtual world environments, wherein the firstdescriptive asset video frames and the second descriptive asset videoframes include a common subset of illustrative asset video frames sothat subsequent utilization of the common subset of illustrative assetvideo frames reduces rendering of other first and second descriptiveasset video frames, wherein the common subset of illustrative assetvideo frames are selected from a set of illustrative asset video framesrendered from the illustrative asset; select one active virtual worldenvironment of the set of active virtual world environments based on atleast one of the learner requirement and learning assessment results forthe selected lesson package to produce a selected virtual worldenvironment; and render updated first descriptive asset video frames ofthe first descriptive asset and updated second descriptive asset videoframes of the second descriptive asset within the selected virtual worldenvironment to produce a new video stream for the learner, wherein theupdated first descriptive asset video frames and the updated seconddescriptive asset video frames include the common subset of illustrativeasset video frames.
 8. The computing device of claim 7, wherein theprocessor further causes the computing device to: generate a firstinstance of execution of the selected lesson package in a first activevirtual world environment; and generate a second instance of executionof the selected lesson package in a second active virtual worldenvironment.
 9. The computing device of claim 7, wherein the processorfurther causes the computing device to: evaluate for at least some ofthe set of active virtual world environments, at least one of learnerinteraction information and environmental sensor information associatedwith execution of an associated instance of execution of the selectedlesson package to produce the learning assessment results for theselected lesson package.
 10. The computing device of claim 7, whereinthe processor further causes the computing device to: output, via theinterface, the new video stream to a second computing device associatedwith the learner.
 11. The computing device of claim 7, wherein theprocessor causes the computing device to select the one active virtualworld environment of the set of active virtual world environments basedon at least one of the learner requirement and the learning assessmentresults for the selected lesson package to produce the selected virtualworld environment by one or more of: identifying a first learningassessment result of a first active virtual world environment thatexceeds a minimum learning assessment result expectation thresholdlevel, wherein the learning assessment results include the firstlearning assessment result, wherein the first active virtual worldenvironment includes the selected virtual world environment; identifyinga second learning assessment result of a second active virtual worldenvironment that exceeds the first learning assessment result of thefirst active virtual world environment, wherein the learning assessmentresults include the second learning assessment result, wherein thesecond active virtual world environment includes the selected virtualworld environment; comparing the learner requirement to estimatedexperience expectations associated with at least some of the set ofactive virtual world environments to identify the one active virtualworld environment that is estimated to deliver more than a minimumthreshold level of sub-requirements of the learner requirement; andcomparing the learner requirement to the estimated experienceexpectations associated with the at least some of the set of activevirtual world environments to identify the one active virtual worldenvironment that is estimated to deliver a highest number of thesub-requirements of the learner requirement.
 12. The computing device ofclaim 7, wherein the processor causes the computing device to render theupdated first descriptive asset video frames of the first descriptiveasset and the updated second descriptive asset video frames of thesecond descriptive asset within the selected virtual world environmentto produce the new video stream for the learner by: selecting the commonsubset of illustrative asset video frames to produce a first portion ofthe updated first descriptive asset video frames of the firstdescriptive asset and to produce a first portion of the updated seconddescriptive asset video frames of the second descriptive asset, so thatsubsequent utilization of the common subset of illustrative asset videoframes reduces rendering of other updated first and second descriptiveasset video frames; rendering a representation of the first set ofknowledge bullet-points within the selected virtual world environment toproduce a remaining portion of the updated first descriptive asset videoframes of the first descriptive asset, wherein the updated firstdescriptive asset video frames include the common subset of illustrativeasset video frames; rendering a representation of the second set ofknowledge bullet-points within the selected virtual world environment toproduce a remaining portion of the updated second descriptive assetvideo frames of the second descriptive asset, wherein the updated seconddescriptive asset video frames includes the common subset ofillustrative asset video frames; and linking the updated firstdescriptive asset video frames of the first descriptive asset with theupdated second descriptive asset video frames of the second descriptiveasset to form at least a portion of the new video stream.
 13. Anon-transitory computer readable memory comprises: a first memoryelement that stores operational instructions that, when executed by aprocessor, causes the processor to: select a lesson package from aplurality of lesson packages based on a learner requirement for alearner to produce a selected lesson package, wherein the learnerrequirement includes at least one of a topic requirement of a topic anda virtual world requirement, wherein the selected lesson packageincludes a first learning object and a second learning object, whereinthe first learning object includes a first set of knowledgebullet-points regarding a first piece of information regarding thetopic, wherein the second learning object includes a second set ofknowledge bullet-points regarding a second piece of informationregarding the topic, wherein the first and second learning objectsinclude an illustrative asset based on the first and second set ofknowledge bullet-points, wherein the illustrative asset depicts anaspect regarding the topic pertaining to the first and second pieces ofinformation, wherein the first learning object further includes a firstdescriptive asset regarding the first piece of information based on thefirst set of knowledge bullet-points and the illustrative asset, whereinthe second learning object further includes a second descriptive assetincluding the second piece of information based on the second set ofknowledge bullet-points and the illustrative asset; a second memoryelement that stores operational instructions that, when executed by theprocessor, causes the processor to: identify a set of active virtualworld environments that each include a different instance of executionof the selected lesson package, wherein a first instance of execution ofthe selected lesson package includes first descriptive asset videoframes of the first descriptive asset and second descriptive asset videoframes of the second descriptive asset within a first active virtualworld environment of the set of active virtual world environments,wherein the first descriptive asset video frames and the seconddescriptive asset video frames include a common subset of illustrativeasset video frames so that subsequent utilization of the common subsetof illustrative asset video frames reduces rendering of other first andsecond descriptive asset video frames, wherein the common subset ofillustrative asset video frames are selected from a set of illustrativeasset video frames rendered from the illustrative asset; a third memoryelement that stores operational instructions that, when executed by theprocessor, causes the processor to: select one active virtual worldenvironment of the set of active virtual world environments based on atleast one of the learner requirement and learning assessment results forthe selected lesson package to produce a selected virtual worldenvironment; and a fourth memory element that stores operationalinstructions that, when executed by the processor, causes the processorto: render updated first descriptive asset video frames of the firstdescriptive asset and updated second descriptive asset video frames ofthe second descriptive asset within the selected virtual worldenvironment to produce a new video stream for the learner, wherein theupdated first descriptive asset video frames and the updated seconddescriptive asset video frames include the common subset of illustrativeasset video frames.
 14. The non-transitory computer readable memory ofclaim 13 further comprises: a fifth memory element that storesoperational instructions that, when executed by the processor causes theprocessor to: generate a first instance of execution of the selectedlesson package in a first active virtual world environment; and generatea second instance of execution of the selected lesson package in asecond active virtual world environment.
 15. The non-transitory computerreadable memory of claim 13 further comprises: a sixth memory elementthat stores operational instructions that, when executed by theprocessor causes the processor to: evaluate for at least some of the setof active virtual world environments, at least one of learnerinteraction information and environmental sensor information associatedwith execution of an associated instance of execution of the selectedlesson package to produce the learning assessment results for theselected lesson package.
 16. The non-transitory computer readable memoryof claim 13 further comprises: a seventh memory element that storesoperational instructions that, when executed by the processor causes theprocessor to: output the new video stream to a computing entityassociated with the learner.
 17. The non-transitory computer readablememory of claim 13, wherein the processor performs functions to executethe operational instructions stored by the third memory element to causethe processor to select the one active virtual world environment of theset of active virtual world environments based on at least one of thelearner requirement and the learning assessment results for the selectedlesson package to produce the selected virtual world environment by oneor more of: identifying a first learning assessment result of a firstactive virtual world environment that exceeds a minimum learningassessment result expectation threshold level, wherein the learningassessment results include the first learning assessment result, whereinthe first active virtual world environment includes the selected virtualworld environment; identifying a second learning assessment result of asecond active virtual world environment that exceeds the first learningassessment result of the first active virtual world environment, whereinthe learning assessment results include the second learning assessmentresult, wherein the second active virtual world environment includes theselected virtual world environment; comparing the learner requirement toestimated experience expectations associated with at least some of theset of active virtual world environments to identify the one activevirtual world environment that is estimated to deliver more than aminimum threshold level of sub-requirements of the learner requirement;and comparing the learner requirement to the estimated experienceexpectations associated with the at least some of the set of activevirtual world environments to identify the one active virtual worldenvironment that is estimated to deliver a highest number of thesub-requirements of the learner requirement.
 18. The non-transitorycomputer readable memory of claim 13, wherein the processor performsfunctions to execute the operational instructions stored by the fourthmemory element to cause the processor to render the updated firstdescriptive asset video frames of the first descriptive asset and theupdated second descriptive asset video frames of the second descriptiveasset within the selected virtual world environment to produce the newvideo stream for the learner by: selecting the common subset ofillustrative asset video frames to produce a first portion of theupdated first descriptive asset video frames of the first descriptiveasset and to produce a first portion of the updated second descriptiveasset video frames of the second descriptive asset, so that subsequentutilization of the common subset of illustrative asset video framesreduces rendering of other updated first and second descriptive assetvideo frames; rendering a representation of the first set of knowledgebullet-points within the selected virtual world environment to produce aremaining portion of the updated first descriptive asset video frames ofthe first descriptive asset, wherein the updated first descriptive assetvideo frames include the common subset of illustrative asset videoframes; rendering a representation of the second set of knowledgebullet-points within the selected virtual world environment to produce aremaining portion of the updated second descriptive asset video framesof the second descriptive asset, wherein the updated second descriptiveasset video frames includes the common subset of illustrative assetvideo frames; and linking the updated first descriptive asset videoframes of the first descriptive asset with the updated seconddescriptive asset video frames of the second descriptive asset to format least a portion of the new video stream.